Journal of Machine Learning Research 2 (2002) 559-594 Submitted 9/01; Published 3/02

Memory-Based Shallow Parsing

Erik F. Tjong Kim Sang erikt@uia.ua.ac.be

CNTS - Language Technology Group University of Antwerp Universiteitsplein 1 B-2610 Wilrijk, Belgium

Editors: James Hammerton, Miles Osborne, Susan Armstrong and Walter Daelemans

Abstract

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement. Keywords: shallow parsing, memory-based learning, feature selection, system combination

1. Introduction

Memory-based learners classify data based on their similarity to data that they have seen earlier. They have been used for a variety of natural language processing tasks with good results, for example for grapheme-to-phoneme conversion (Hoste et al., 2000), stress assignment (Daelemans et al., 1994) and word class tagging (Van Halteren et al., 2001). These natural language processing tasks are classification tasks: they require an assignment of a class to each character or to each word. Shallow parsing is more complicated than that: it requires sequences of words to be grouped together and be classified.

We believe that all natural language tasks can be performed successfully by memorybased learners. Identifying and classifying sequences of words can be converted to a classification task by using special tag sets, for example the IOB tag set proposed by Ramshaw and Marcus (1995). Parsing requires different processing levels and these can be simulated by cascading several memory-based learners which have been trained on different subtasks (Daelemans, 1995). The idea of using memory-based methods for processing natural language has recently led to the emergence of a new paradigm: Memory-Based Language Processing (MBLP) to which a special issue of the Journal of Experimental & Theoretical Artificial Intelligence was devoted (Daelemans, 1999).

The goal of this paper is to test the theoretic ideas about memory-based learning applied to natural language tasks, in particular its application to shallow parsing. We will implement the ideas of Daelemans (1995), show what problems need to be solved, test memory-based shallow parsers and compare their performance with those of other systems. The tasks which

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Tjong Kim Sang

we will examine are identification of base noun phrases, recognition of phrases of arbitrary types, finding clauses, discovering embedded noun phrases and full parsing. Memory-based learning performs well on natural language tasks that require output that has relatively little structure. In this paper we will investigate whether we can obtain equally good results when it is applied to tasks requiring more complex outputs.

2. Approach

In our approach we will use three techniques. We will use memory-based learning as base classification method for assigning linguistic classes to data. We will attempt to solve a weakness of this approach, disregarding irrelevant features, by using an additional feature selection method. Finally, we will examine the combination of several learners in order to obtain an extra performance boost. This section also contains information about evaluation and system configuration for performing parameter tuning.

2.1 Memory-Based Learning

The basic idea behind memory-based learning is that concepts can be classified by their similarity with previously seen concepts. In a memory-based system, learning amounts to storing the training data items. The strength of such a system lies in its capability to compute the similarity between a new data item and the training data items. The most simple similarity metric is the overlap metric (Daelemans et al., 2000). It compares corresponding features of the data items and adds 1 to a similarity rate when they are different. The similarity between two data items is represented by a number between zero and the number of features, n, in which value zero corresponds with an exact match and n corresponds with two items which share no feature value. Here is an example:

TRAIN1 man saw the V
TRAIN2 the saw . N
TEST1 boy saw the ?

It contains two training items of a part-of-speech (POS) tagger and one test item for which we want to obtain a POS tag. Each item contains three features: the word that needs to be tagged (saw) and the preceding and the next word. In order to find the best POS tag for the test item, we compare its features with the features of the training data items. The test item shares two features with the first training data item and one with the second. The similarity value for the first training data item (1) is smaller than that of the second (2) and therefore the overlap metric will prefer the first.

A weakness of the overlap metric is that it regards all features as equally valuable for computing similarity values. Generally some features are more important than others. For example, when we add a line “TRAIN3 boy and the C” to our training data, the overlap metric will regard this new item as equally important as the first training item. Both the first and the third training item share two feature values with the test item but we would like the third to receive a lower similarity value because it does not contain the word for which we want find a POS tag (saw). In order to accomplish this, we assign weights to the features in such a way that the second feature receives a higher weight than the other two.

Memory-Based Shallow Parsing

The method which we use to assign weights to the features is called Gain Ratio, a normalized variant of information gain (Daelemans et al., 2000). It estimates feature weights by examining the training data and determines for each feature how much information it contributes to the knowledge of the classes of the training data items. The weights are normalized in order to account for features with many different values. The Gain Ratio computation of the weights is summarized in the following formulas:

w_{i} = \frac{H(C) − ∑_{v∈V_{i}} P (v) × H(C | v)}{H(V_{i})}  (1)
H(X) = − ∑_{x∈X} P (x)log_{2}P (x)  (2)

Here w_{i} is the weight of feature i, C the set of class values and V_{i} the set of values that feature i can take. H(C) and H(V_{i}) are the entropy of the sets C and V_{i} respectively and H(C | v) is the entropy of the subset of elements of C that are associated with value v of feature i. P (v) is the probability that feature i has value v. The normalization factor H(V_{i}) was introduced to prevent that features with low generalization capacities, like identification codes, would obtain large weights.

The memory-based learning software which we have used in our experiments, TiMBL (Daelemans et al., 2000), contains several algorithms with different parameters. In this paper we have restricted ourselves to using a single algorithm (k nearest neighbor classification) with a constant parameter setting. It would be interesting to evaluate every algorithm with all of its parameters but this would require a lot of extra work. We have changed only one parameter of the nearest neighbor algorithm from its default value: the size of the nearest neighborhood region. The learning algorithm computes the distance between the test item and the training items. The test item will receive the most frequent classification of the nearest training items (nearest neighborhood size is 1). Daelemans et al. (1999) show that using a larger neighborhood is harmful for classification accuracy for three language tasks but not for noun phrase chunking, a task which is central to this paper. In our experiments we have found that using the three nearest sets of data items leads to a better performance than using only the nearest data items. This increase of the neighborhood size used leads to a form of smoothing which can get rid of the influence of some data inconsistencies and exceptions.

2.2 Feature Selection

A disadvantage of the Gain Ratio metric used in memory-based learning is that it computes a weight for a feature without examining other available features. If features are dependent, this will generally not be reflected in their weights. A feature that contains some information about the classification class on its own, but none when another more informative feature is present will receive a non-zero weight. Features which contain little information about the classification class will receive a small weight but a large number of them might still overrule more important features. These two problems will have a negative influence on the classification accuracy, in particular when there are many features available.

We have tested the capacity of Gain Ratio to deal with irrelevant features by using it for a simple binary classification problem with extra random features. The problem which Figure 1: Average number of correct patterns over 1000 runs of a memory-based learner us-

ing the Gain Ratio metric for test data containing 400 XOR patterns after adding 0 to 10 random binary features. The system performs perfectly with one random feature but when two or more random features are added, the performance drops to about half for 10 extra features.

we chose is the XOR problem. It contains two binary (0/1) features and a pair of these feature values should be classified as 0 when the values are equal and as 1 when the features are different. We have created training and test data which contained 100 examples of the four possible patterns (0/0/0, 0/1/1, 1/0/1 and (1/1/0). A memory-based learner which uses Gain Ratio was able to correctly classify all 400 patterns in the training data. After this we added ten random binary features to both the training data and the test data and observed the performance. The average results of 1000 runs can be found in Figure 1.

Without extra features the memory-based learner performs perfectly. Adding a random feature does not harm its performance but after adding two the system only gets 394 of the 400 patterns correct on average. The performance drops for every extra added feature to about 211 for 10 extra features which is not much better than randomly guessing the classes. This small experiment shows that Gain Ratio has difficulty with feature sets that contain many irrelevant features. We need an extra method for determining which features are not necessary for obtaining a good performance.

Aha and Bankert (1994) give a good introduction to methods for selecting relevant features for machine learning tasks. The methods can be divided in two groups: filters and wrappers. A filter uses an evaluation function for determining which features could be more relevant for a classifier than others. A wrapper finds out if one feature is more important than another by applying the classifier to data with either one of the features and comparing the results. This is requires more time than the filter approach but it generates better feature sets because it cannot suffer from a bias difference which may exist between the evaluation function and the classifier (John et al., 1994).

Both the filter and the wrapper method start with a set of features and attempt to find a better set by adding or removing features and evaluating the resulting sets. There are two basic methods for moving through the feature space. Forward sequential selection starts with an empty feature set and evaluates all sets containing one feature. After this it selects the one with the best performance and evaluates all sets with two features of which one is the best single feature. Backward sequential selection starts with all features and evaluates all sets with one feature less. It will selects the one with the best performance and then examines all feature sets which can be derived from this one by removing one feature. Both methods continue adding or removing a feature until they cannot improve the performance.

Forward and backward sequential selection are a variant of hill-climbing, a well-known search technique in artificial intelligence. As with hill-climbing, a disadvantage of these methods is that they can get stuck in local optima, in this case a non-optimal feature set which cannot be improved with the method used. In order to minimize the influence of local optima, we use a combination of the two methods when examining feature sets: bidirectional hill-climbing (Caruana and Freitag, 1994). The idea here is to apply both adding a feature and removing a feature at each point in the feature space. This enables the feature selection method to backtrack from nonoptimal choices. In order to keep processing times down we will start with an empty feature list just like in forward sequential selection.

2.3 System Combination

When different machine learning systems are applied to the same task, they will make different errors. The combined results of these systems can be used for generating an analysis for the task that is usually better than that of any of the participating systems, for example by choosing pattern analyses selected by the majority of the systems. This approach will eliminate errors that made by a minority of the systems. Here is a made-up example: suppose we have five systems, c_{1} - c_{5}, which assign binary classes to patterns. Their output for eight patterns, p_{1} - p_{8}, is as follows:

c_{1} c_{2} c_{3} c_{4} c_{5} correct
p_{1} 0 0 0 0 0 0
p_{2} 1 1 1 1 1 1
p_{3} 0 0 0 0 0 0
p_{4} 1 0 1 1 1 1
p_{5} 0 0 1 0 0 0
p_{6} 1 1 1 1 0 1
p_{7} 1 0 0 0 0 0
p_{8} 1 1 1 0 1 1

Each of the five systems makes an error. We can use a combination of the five by choosing the class that has been predicted most frequently for each pattern. For the first three patterns this will not make a difference because all systems predict the same class. For pattern 4 we will choose class 1, thereby eliminating an error of classifier 2. Pattern 5 will be associated with class 0, thus eliminating classifier 3’s only error. Patterns 6, 7 and 8 will receive classes 1, 0 and 1 respectively, thereby eliminating errors of classifiers 5, 1 and 4. Thus the majority choice will generate a perfect analysis of the data.

In this paper we will evaluate different techniques for combining system output, most of which have been put forward by Van Halteren et al. (2001). We use four voting methods and three stacked classifiers. Voting methods assign weights to the output of the individual systems and for each pattern choose the class with the largest accumulated score. The most simple voting method is the one we have used in the preceding example: Majority Voting. It gives all systems the same weight. A more elaborate method is accuracy voting (TotPrecision). It assigns a weight to each system which is equal to the accuracy of the system on some evaluation data.

Some classes might be easier to predict than other classes and for this reason we have also tested two voting methods which use weights based on accuracies for particular class tags. The first is TagPrecision. For each output value v of system s, it uses a weight which is equal to the precision of that system s obtained for this value v. The second method is Precision-Recall. It starts from the same weights as TagPrecision but adds to these the probability that systems producing different output values would have missed v. For example, suppose that there are two systems s_{1} and s_{2}, and that for some data item s_{1} predicts value v_{1} while s_{2} predicts something else. In that case, the probability that s_{1} is right is precision(s_{1}, v_{1}) while the probability that s_{2} would have missed v_{1} is 1 − recall(s_{2}, v_{1}). Precision-Recall will assign the weight precision(s_{1}, v_{1}) + (1 − recall(s_{2}, v_{1})) to the event of s_{1} predicting v_{1}.

A stacked classifier is a classifier which processes the results of other classifiers. We have used three variants of stacked classifiers. The first is called TagPair. It examines pairs of values produced by two systems and estimates the probability that a certain output value is associated with the pair. In the case of the two systems s_{1} and s_{2} producing two distinct values v_{1} and v_{2}, TagPair will examine evaluation data and find that the value pair is associated with, for example, v_{1} in 20% of the cases, v_{2} in 70% and v_{3} in 10%. These numbers will be used as weights for the three output values and the one that has accumulated the largest value after examining all value pairs in the pattern, will be selected. Unlike the voting methods, TagPair has the opportunity to choose the correct output tag even if all systems have made an incorrect prediction (for example, v_{3} in this example).

The other stacked classifier which we have evaluated is the memory-based learner itself. We have tested it in two modes: one in which only the output of the systems was included and one in which we included information about the test item. This extra information was the word that needed to be classified, its part-of-speech (POS) tag and the context (words/POS tags) in which it appeared. The memory-based learner used the same settings as described earlier in this section: it used the Gain Ratio metric and examined a nearest neighborhood of size three.

The weight assignment methods used by the voting methods and the stacked classifiers suffer from the same problem as Gain Ratio: they might fail to disregard irrelevant features. For this reason we have often tested the combination methods both with all available system results as well as with a subset of these, thus mimicking the feature selection method described earlier. Apart from Majority Voting, all voting methods and stacked classifiers require training data. This means that we need both training data for the individual systems and training data for the combinators. We will describe how we have selected the training data in the next section.

2.4 Parameter Tuning

In this paper, we will compare different learner set-ups and apply the best one to standard data sets. For example, we will examine different data representations and test different system combination techniques. We should be careful not to tune the system to the test data and therefore we will only use the available training data for finding the best configuration for the learner. This can be done by using 10-fold cross-validation (Weiss and Kulikowski, 1991). The training data will be divided in ten sections of similar size and each section will be processed by a system which has been trained on the other nine. The overall performance on all ten sections will be regarded as the performance of the system.

In our experiments, we will process the data twice. First we will let the learner generate a classification of the data. After this the learner will process the data another time, this time while including the classifications found earlier for the context of a data item. While working with n-fold cross-validation, we should be careful that information from a test part is not accidentally used in its training part. In the first processing phase we will generate classes for the first section while using the other nine sections. Thus information about the classes in, for example, section two is encoded in the classes produced in section one. If in the second phase we use the classifications of the first section while processing section two, we are analyzing a section while having access to (indirect) information about the classes in the data. Information about the classes in section two might leak to this process via the training data, something which is undesired.

There are two ways for preventing this form of information leaking. Both concern being more strict when it comes to creating the training data of the second system. In a cascaded 10-fold cross-validation experiment, the second phase training data for section x must be constructed without using this section. This means that instead of running one 10-fold cross-validation experiment with the first system, we need to run ten 9-fold cross-validation experiments in order to obtain correct training data for the ten sections in the second system. Section one will be trained with the 9-fold cross-validation results from sections 2-10, section 2 with 1 and 3-10 and so on. If at any time we need to add a third phase to the cascade of systems, we need to run 8-fold cross-validation experiments with the first system and 9-fold cross-validation experiments with the second. For extra systems the number of extra runs increases and the amount of available training data for the first system decreases.

The second method for preventing training information from a processing phase leaking to the classifications of a next phase is by only using results from previous phases in the test data. In the training data we use the perfect classes rather than the output of the previous phase. This has two disadvantages. First, we cannot use a feature containing the class of the focus word because this feature is the same as the output class. This means that we can only use the classes of neighboring words. Second, the opportunity to correct errors made in the first phase will be restricted because the training data no longer contains information about the errors made by this phase. The advantage of this approach is that we can use all training data in all training phases, so the problem of a diminishing quantity of training data disappears. This approach is especially useful with longer cascades of learners, as for example is required in full parsing.

Here is an example to illustrate the two methods: suppose a word in the sixth section in the second phase of a ten-fold cross-validation experiment in chunking is represented by the following eight features:

w_{i−1} w_{i} w_{i+1} p_{i−1} p_{i} p_{i+1} c_{i−1} c_{i+1}

The goal is to find a chunk tag for word w_{i}. The word features w_{i}, w_{i−1} and w_{i+1} represent, the word itself, the preceding word and the next word, respectively. The POS tag features p_{i}, p_{i−1} and p_{i+1} contain the POS tags of the three words. The two chunk features c_{i−1} and c_{i+1} hold the chunk tag of the preceding and the next word. The word and POS tag information have been taken from the training data. In the first method, the two chunk features are computed by a preceding phase. If this item is part of the training data for section x, c_{i−1} and c_{i+1} were generated by a nine-fold cross-validation experiment which uses all sections except section x. This means that the two chunk features have been generated by training with all sections except 6 and x. If the item is part of the test data, then the chunk features are computed by a ten-fold cross-validation experiment (training with sections 1-5 and 7-10). The second method generates chunk features for the test data in the same way but for training data it takes c_{i−1} and c_{i+1} from the training data, thus preventing that they contain implicit information about the test sections.^{1}

2.5 Evaluation

We will compare the results of a shallow parser with an available hand-parsed corpus. For this purpose we will use the precision and recall of the phrases in the results. Precision is the percentage of phrases found by the learner that are correct according to the corpus. Recall is the percentage of corpus phrases found by the learner. It is easier to optimize a system configuration based on one evaluation score and therefore we combine precision and recall in the F_{β} rate (Van Rijsbergen, 1975):

F_{β} = \frac{(β^{2} + 1) ∗ precision ∗ recall}{β^{2} ∗ precision + recall}  (3)

β can be used for giving precision a larger (β >1) or smaller (β <1) weight than recall. We do not have a preference for one or the other and therefore we use β=1. In previous work on shallow parsing, often a word-related accuracy rate is used as evaluation criterion. We do not believe that this is a good method for evaluating results of phrase detection algorithms. Accuracy rates assign positive values to correctly identified non-phrase words and to partially identified phrases. Furthermore they will produce different numbers for the same analysis based on the data representation used. For these reasons, the relation between accuracy rates and F_{β} rates is poor and preference should be given to using the latter.

Accuracy rates have one advantage over F_{β} rates: standard statistical tests can be used for determining if the difference between two accuracy rates is significant. Accuracy is a relatively simple function correct/processed where processed is the number of items that have been processed and correct is the number of items that received the correct class.

1. In case c_{i−1} is part of a previous section or c_{i+1} is in a next section, they are left empty.

Unfortunately, F_{β=1} is more complex: after some arithmetic we get 2 ∗ correct/(found + corpus) where found is the number of phrases found by the learner, correct the number of phrases found that were correct and corpus the number of phrases in the corpus according to some gold standard. The value of the corpus variable is an upper bound on the variable correct. The complexity of the F_{β=1} computation makes it hard to apply standard statistical tests to F_{β=1} rates.

Yeh (2000) offers a method for computing significance values for F_{β=1} rate comparisons: by using computationally-intensive randomization tests. His approach requires test data classifications for all systems that need to the compared. Usually we only have access to the test data classifications of our own system and therefore we have used a variant of these randomization tests presented: bootstrap resampling (Noreen, 1989). The basic idea of this approach is to regard the test data classifications as a population of cases. A random sample of this population can be created by arbitrarily choosing cases with replacement. We can create many random samples of the same size as the test data and compute an average F_{β=1} rate over the samples and a standard deviation for this average. These statistical measures can be used for deciding if the performance of another system is significantly different from our system. Since we do not know if the performance of our system is distributed according to a normal distribution, we will determine significance boundaries in such a way that 5% of the samples evaluate worse (or better) than the chosen boundary.

3. Chunking

In this section we will apply a memory-based learner to chunking, identifying base phrases. The section starts with a some background information on this task. After this we will present the results of our experiments with base noun phrase identification and our work targeted at finding base phrases of arbitrary types.

3.1 Task Overview

A text chunker divides sentences in phrases which consist of a sequence of consecutive words which are syntactically related. The phrases are nonoverlapping and nonrecursive. In the beginning of the nineties, Abney (1991) suggested to use chunking as a preprocessing step of a parser. Ten years later, most statistical parsers contained a chunking phase (for example Ratnaparkhi (1998)). In this study, we will divide chunking in two subtasks: finding only noun phrases and identifying arbitrary chunks.

Machine learning approaches towards noun phrase chunking started with work by Church (1988) who used bracket frequencies associated with POS tags for finding noun phrase boundaries in text. In an influential paper about chunking, Ramshaw and Marcus (1995) show that chunking can be regarded as a tagging task. Even more importantly, the authors propose a training and test data set that are still being used for comparing different text chunking methods. These data sets were extracted from the Wall Street Journal part of the Penn Treebank II corpus (Marcus et al., 1993). Sections 15-18 are used as training data and section 20 as test data.^{2} In principle, the noun phrase chunks present in the material are noun phrases that do not include other noun phrases, with initial material (determiners, adjectives, etc.) up to the head but without postmodifying phrases (prepositional phrases or clauses) (Ramshaw and Marcus, 1995).

2. The noun phrase identification data is available from ftp://ftp.cis.upenn.edu/pub/chunker/

The noun phrase chunking data produced by Ramshaw and Marcus (1995) contains a couple of nontrivial features. First, unlike in the Penn Treebank, possessives between two noun phrases have been attached to the second noun phrase rather than the first. An example in which round brackets mark chunk boundaries: ( Nigel Lawson ) (’s restated commitment ): the possessive ’s has been moved from Nigel Lawson to restated commitment. Second, Treebank annotation may result in nonexpected noun phrase annotations: British Chancellor of ( the Exchequer ) Nigel Lawson in which only one noun chunk has been marked. The problem here is that neither British Chancellor nor Nigel Lawson has been annotated as separate noun phrases in the Treebank. Both British ... Exchequer and British ... Lawson are annotated as noun phrases in the Treebank but these phrases could not be used as noun chunks because they contain the smaller noun phrase the Exchequer.

Ramshaw and Marcus (1995) proposed to encode chunks with tags: I for words that are inside a noun chunk and O for words that are outside a chunk. In case one noun phrase immediately follows another one, they use the tag B for the first word of the second phrase in order to show that a new phrase starts there. With the three tags I, O and B any chunk structure can be encoded. This representation has two advantages. First, it enables trainable POS taggers to be used as chunkers by simply changing their training data. Second, it minimizes consistency errors which appear with the bracket representation where open and close brackets generated by the learner may not be balanced. Here is an example sentence first with noun phrases encoded by pairs of brackets and then with the Ramshaw and Marcus IOB representation:

In ( early trading ) in ( Hong Kong ) ( Monday ) , ( gold ) was quoted at ( $ 366.50 ) ( an ounce ) .

In_{O} early_{I} trading_{I} in_{O} Hong_{I} Kong_{I} Monday_{B} ,_{O} gold_{I} was_{O} quoted_{O} at_{O} $_{I} 366.50_{I} an_{B} ounce_{I} ._{O}

Tjong Kim Sang and Veenstra (1999) presents three variants on the Ramshaw and Marcus representation and shows that the bracket representation can also be regarded as a tagging representation with two streams of brackets. They named the variants IOB2, IOE1 and IOE2 and used IOB1 as name for the Ramshaw and Marcus representation. IOB2 was the same as IOB1 but now every chunk-initial word receives tag B. IOE1 differs from IOB1 in the fact that rather than the tag B, a tag E is used for the final word of a noun chunk which is immediately followed by another chunk. IOE2 is a variant of IOE1 in which each final word of a noun phrase is tagged with E. The bracket representations use open brackets for phrase-initial words, close brackets for phrase-final words and a period for all other words. Table 1 contains example tag sequences for all six tag sequences for the example sentence.

The representation variants are interesting because a learner will make different errors when trained with data encoded in a different representation. This means that we can train one learner with five^{3} data representations and obtain five different analyses of the data which we can combine with system combination techniques. Thus the different data

3. The combination of open and close brackets, O+C, will be regarded as one data representation.

IOB1 O I I O I I B O I O O O I I B I O
IOB2 O B I O B I B O B O O O B I B I O
IOE1 O I I O I E I O I O O O I E I I O
IOE2 O I E O I E E O E O O O I E I E O
O  . [ . . [ . [ . [ … [ . [ . .
C  . . ] . . ] ] . ] …. ] . ] .

Table 1: The chunk tag sequences for the example sentence In early trading in Hong Kong Monday , gold was quoted at $ 366.50 an ounce . for six different tagging formats. The I tag has been used for words inside a chunk, O for words outside a chunk, B and [ for chunk-initial words and E, ] for chunk-final words and periods for words that are neither chunk-initial nor chunk-final.

representations may enable us to improve the performance of the chunker. The data representations can be used both for noun phrase chunking and for arbitrary chunking. In the latter task, more than one chunk type exists so the tags need to be expanded with type-specific suffixes. For example: B-VP, I-VP, E-VP, [-VP and ]-VP.

The arbitrary chunking task was more difficult to design because many interesting phrase types often contain parts which belong to other phrases (Tjong Kim Sang and Buchholz, 2000). For example, verb phrases may contain noun phrases and prepositional phrases often include a noun phrase. Furthermore, noun phrases may contain quantitative or adjective phrases which may prevent them from being identified as noun chunks. The noun, verb and prepositional phrases should be included and therefore the following measures have been taken when constructing the data for the arbitrary chunking task: First, a couple of phrase types, for example quantifier phrases and adjective phrases, have been removed from places where they prevented the identification of noun phrases. This made possible annotating more phrases as noun chunks. Second, some phrase types in the annotated data, for example verb phrases and prepositional phrases, lack material that has already been included in a phrase of another type. Third, adjacent verb clusters have been put in one flat verb phrase unlike in the Treebank where often each verb starts a new phrase. And fourth, adverbial phrase boundaries have been removed from adjective phrases and verb phrases to allow all material to be included in the mother phrase.

This chunk definition scheme will generate data in which most of the tokens have been assigned to a chunk of some type. The odd tokens that fall out are usually punctuation signs. This chunk scheme has been used for generating training and test data for the CoNLL-2000 shared task (Tjong Kim Sang and Buchholz, 2000). The data contains the same segments of the Wall Street Journal part of the Penn Treebank as the noun phrase data of Ramshaw and Marcus (1995): sections 15-18 as training data and section 20 as test data.^{4} We will use these data sets in our arbitrary chunking experiments.

The training and the test data contain two types of features: words and POS tags. The words have been taken from the Penn Treebank. The POS tags of the Treebank have been manually checked and therefore they should not be used in the chunking data. In future applications, the chunking process will be applied to a text with POS tags that have been generated automatically. These POS tags will contain errors and therefore the performance of the chunker will be worse than when applied to a Treebank text with manually checked POS tags. If we want to obtain realistic performance rates, we should work with automatically generated POS tags in our shallow parsing experiment. Conform with earlier work like that of Ramshaw and Marcus (1995), we have used POS tags that were generated by the Brill tagger (Brill, 1994).

4. The CoNLL-2000 shared task data is available from http://lcg-www.uia.ac.be/conll2000/chunking/

3.2 Noun Phrase Recognition

We will use a memory-based learner to find noun phrase chunks in text. In order to determine the best configuration for the learner, we will test different system configurations on the standard training data sets put forward by Ramshaw and Marcus (1995). We will evaluate different feature sets for representing words. Additionally, we will use the five data representations for generating different system results and use system combination techniques for combining these results.

In our experiments we will represent words as sets of words and POS tags. These sets contain the word itself, its part-of-speech (POS) tag and a left and right context of a maximum of four words and POS tags on each side, 18 features in total. We have explained in Section 2.2 that memory-based learners equipped with the Gain Ratio metric have difficulty in dealing with irrelevant features. Therefore we will use a feature selection method, bi-directional hill-climbing starting with zero features, for finding the best subset of the 18 features for each different data representation.

The memory-based learner will make two passes over the data. First, it will attempt to predict the noun phrases in the data as well as possible. After this it will use the output of this first pass as information about the noun phrases in the immediate context of the current word. This means that the second pass has access to the 18 features of the first pass plus the chunk tags of the two words immediately in front of the current word and the chunk tags of the two words immediately following the current word. This cascaded approach was chosen because it was useful for improving overall performance in our earlier work (Tjong Kim Sang and Veenstra, 1999). We omitted the chunk tag for the current word because including it gave a negative bias to the chunker performance. Gain Ratio would correctly identify it as a feature which contained a lot of information about the output class and the weight it assigned to it would make it hard for the other features to influence the output class at all (Tjong Kim Sang and Veenstra, 1999).^{5}

We performed a cascaded feature search while using five different data representations on the training data of Ramshaw and Marcus (1995) in a 10-fold cross-validation approach. We prevented information leaking in the second phase conform Section 2.4 by using the estimated chunk tags for test data and using the corpus tags in the training data. In this way we made sure that when the test data consisted of section x, no information about section x was available in the training data. The results of the 10-fold cross-validation

5. The problem of using the predicted class of the current word was a result of an earlier study in which we

did not use feature selection. The selection method used in this study would probably have disregarded

this feature automatically. It would start out as the most informative feature but with the feature on

its own we would get a worse performance than with combinations of other features (we perform feature

selection while keeping the five best combinations).

train  Pass 1  Pass 2
Repr. F_{β=1}  features  F_{β=1}  features
IOB1 91.88 word_{−4..0} POS_{−2..3} 92.54 word_{−2..0} POS_{−4..3} chunk_{−2,−1,1,2}
IOB2 91.78 word_{−1..0} POS_{−4..3} 92.29 word_{−1..0} POS_{−4..2} chunk_{−1,1,2}
IOE1 91.64 word_{0..1} POS_{−3..3} 92.28 word_{0..1} POS_{−3..3} chunk_{−1,1,2}
IOE2 92.19 word_{−3..4} POS_{−4..4} 92.59 word_{0..1} POS_{−1..3} chunk_{−2,−1,1,2}
O  96.04 word_{−2..0} POS_{−4..3} 96.11 word_{−1,0} POS_{−4..1} chunk_{−1,2}
C  96.43 word_{0..4} POS_{−4..4} 96.45 word_{0..2} POS_{−4..2} chunk_{−2,−1,1}

Table 2: Best F_{β=1} found for six data representations in two passes while using a bidirectional hill-climbing feature search algorithm in a 10-fold cross-validation process applied to the training data for the noun phrase chunking task. Note that the rates obtained for the O (open bracket) and C (close bracket) representations are for phrase starts and phrase ends respectively and thus higher than for the first four which evaluate complete phrase identification.

experiments can be found in Table 2. In the best feature sets of the first pass most of the nine POS tag features are used (almost eight on average) but interestingly enough only a few of the word features (just over four on average). The best sets for the second pass use fewer POS tag features (under seven), fewer word tags (just over two) and most of the chunk features (about three). The table shows that a wide context is more important for the POS features than for the chunk features and less important for the word features.

Our motive for processing six representations rather than one was to obtain different results which we could combine in order to improve performance. System combination can be seen as a second cascade behind passes one and two. For reasons mentioned in Section 2.4, adding a second cascade in a 10-fold cross-validation experiment requires taking extra care to prevent information leaking from a training data at one level to the training data of the next level. We have taken care of this problem by preparing the training data of the combination techniques with 9-fold cross-validation runs which were independent of the 10-fold cross-validation experiments used for generating the test data. For example, the test data for the first section was generated by training with sections 2-10 twice, first without information about context chunk tags and then with the perfect information of the context chunk tags. The training data was generated with a 9-fold cross-validation process on sections 2-10, also first without context chunk tags and then with perfect context chunk tags. By working this way it was impossible for information about the first section to enter the training data of the combination processes.

Most system combination techniques require results that are in the same format. We have results in six different formats which means that we need to convert them to one format. Since we do not know which of the formats would suit the combination process best, we have evaluated all formats. The four IO formats can trivially be converted to each other and to the O and the C format. The conversion of the two bracket formats to the other four is nontrivial. The two data streams have been generated independently of each other and this means that they may contain inconsistencies. We have chosen to get rid of

train  IOB1 IOB2 IOE1 IOE2 O+C

all systems

Majority  93.06 93.06 93.14 93.12 93.35
TotPrecision  93.06 93.05 93.13 93.05 93.35
TagPrecision  93.06 93.10 93.11 93.11 93.35
Precision-Recall 93.06 93.10 93.11 93.08 93.35
TagPair  93.05 93.14 93.10 93.13 93.36
MBL  93.14 93.12 93.07 92.92 93.35
MBL+  92.81 92.74 92.91 92.78 93.29

some systems

Majority  93.02 93.12 93.08 92.99 93.37
TotPrecision  93.02 93.12 93.08 92.99 93.37
TagPrecision  93.04 93.13 93.10 92.99 93.37
Precision-Recall 93.04 93.13 93.13 93.04 93.37
TagPair  93.08 93.16 93.12 93.05 93.37
MBL  93.12 93.18 93.18 93.03 93.38

Table 3: F_{β=1} rates obtained on 10-fold cross-validation experiments on the noun phrase chunking data while combining results obtained with five different data representations. All five representations have been tested and best rates have been obtained while using the the combined bracket representation O+C. All combination results are better than any result of the individual systems (92.59, see Table 2) and generally combing five systems led to better results than when only three or four were used. The best results have been obtained with a stacked memory-based classifier that used all system results except those generated with IOE1. However, the performance differences are small.

these by removing all brackets which cannot be matched with the closest candidate. For example, if we have a structure like ( a ( b c ) d ) then the first bracket will be removed because it cannot be matched with the second bracket. The second and third will be kept because they match. Finally, the fourth will be removed because it cannot be matched with the third. We obtain the balanced structure a ( b c ) d which can trivially be converted to the four IO formats.

We have combined the five results of pass two of the 10-fold cross-validation experiments on the noun phrase chunking training data (O and C have now been regarded as one data stream O+C). We have used the system combination techniques described in Section 2.3: Majority Voting, TotPrecision, TagPrecision, Precision-Recall, TagPair and two variants of a stacked memory-based learner. The first stacked learner did not use any context information while the second one had access to a limited amount of context information: the current word, the current POS tag or pairs containing the current POS tag and one of the three current word, previous POS tag or next POS tag. We have performed combination experiments with all five data streams and with all subsets of three and four data streams. The results can be found in Table 3. For the second stacked classifier we only included the best results (obtained with context feature current POS tag). System combination improved performance: the worst result of the combination techniques is still better than the best result of the individual systems. The differences between the combination techniques are small. Furthermore, system combination with the four IO data representations leads to similar results but the combined bracket representation consistently obtains higher F_{β=1} rates. It should be noted though that while combination of the data with the IO representations leads to similar precision and recall figures, O+C obtains its higher F_{β=1} rates with high precision rates and lower recall rates.

Since the performance differences between the combination techniques displayed in Table 3 are small, we are relatively free in selecting a technique for further processing. We chose Majority Voting because it is the simplest of the combination techniques that were tested since it does not require extra combinator training data like the other techniques. It does seem reasonable to use the O+C representation during the combination process because the best results have been obtained with this representation. We will restrict ourselves to a few systems rather than combining all because Majority Vote in combination with the O+C representation obtained a slightly higher F_{β=1} rate that way. The best rate was obtained while using only the systems with data representations IOB1, IOE2 and O+C so we restrict ourselves to these three. This leaves us with the following processing scheme:

1. Process the test data with a memory-based model generated from the training data. Use the features shown in Table 2 (Pass 1) and generate output data streams while using the representations IOB1, IOE2, O and C.

2. Perform a second pass over the test data with another memory-based model obtained from the training data. Again use the features shown in Table 2 (Pass 2). In the test data, use the estimated chunk tags from the previous run as chunk tag features and in the training data use the corpus chunk tags as chunk features. Perform these passes four times, once for each of the data representations IOB1, IOE2, O and C.

3. Convert the output for the data representations IOB1 and IOE2 to the O and the C format.

4. Combine the three O data streams (IOB1, IOE2 and O) with Majority Voting and do the same for the three C data streams (IOB1, IOE2 and C).

5. Remove brackets from the resulting O and C data streams which cannot be matched with other brackets. The balanced bracket structure is the analysis of the test data that is the output of the complete system.

We have applied this procedure to the data sets of Ramshaw and Marcus (1995): sections 15-18 of the Wall Street Journal part of the Penn Treebank (Marcus et al., 1993) as training data and section 20 of the same corpus as test data. The system obtained a F_{β=1} rate of 93.34 (precision 94.01% and recall 92.67%). This is a modest improvement of our earlier work (Tjong Kim Sang, 2000a) in which we did not use feature selection and where we obtained an F_{β=1} rate of 93.26. In order to estimate significance thresholds, we have applied a bootstrap resampling test to the output of our system. We created 10,000 populations by randomly drawing sentences with replacement from the system results. The number of sentences in each population was the same as in the test corpus. The average F_{β=1} of the 10,000 populations was 93.33 with a standard deviation of 0.24. For 5 percent of the populations, the F_{β=1} rate was equal to or lower than 92.93 and for another 5 percent it was equal to or higher than 93.73. Since 93.26 is between the two significance boundaries, our current system does not perform significantly better than the previous version without feature selection.

3.3 Arbitrary Phrase Identification

Our work with chunks of arbitrary types^{6} is similar to that with noun phrase chunks apart from two facts. First, we refrained from using feature selection methods. Applying these methods did not gain us much for noun phrase chunking but they required a lot of extra computational work. Therefore we went back to using a fixed set of features in these experiments. The context size we used here was four left and four right for words and POS tags in the first pass over the data, and three left and three right for words and POS tags, and two left and two right without the focus for chunk tags in the second pass. This means that both first and second pass use 18 features. The second pass has only been used for the four IO data representations. Table 2 shows that the second pass improved the performance of the first pass only by a small margin for the two bracket representations O and C.

The second difference between this study and the one for noun phrase chunks originates from the fact that apart from chunk boundaries, we need to find chunk types as well. We can approach this task in two ways. First, we could train the learner to identify both chunk boundaries and chunk types at the same time. We have called this approach the Single-Phase Approach. Second, we could split the task and train a learner to identify all chunk boundaries and feed its output to another classifier which identifies the types of the chunks (Double-Phase Approach). A computationally-intensive approach would be to develop learners for each different chunk type. They could identify chunks independently of each other and words assigned to more than one chunk could be disambiguated by choosing the chunk type that occurs most frequently in the training data (N-Phase Approach). Since we did not know in advance which of these three processing strategies would generate the best results, we have evaluated all three.

In order to find the best processing strategy and the best combination technique, we have performed several 10-fold cross-validation experiments on the training data. We have processed this data for each processing strategy and in each of the six data representations earlier used for noun phrase chunking. After this we have used the seven combination techniques presented in Section 2.3 for combining these. The results can be found in Table 4. Of the three processing strategies, the N-Phase Approach generally performed best with Double-Phase being second best and Single-Phase performing worst. Again, system combination improved all individual results. There were only small differences between the seven combination techniques when compared for the same processing approach. The only exception were the two stacked MBL classifiers applied to the Single-Phase Approach results. They did about 0.3 F_{β=1} rate better than most of the other combination techniques.

6. The results of our arbitrary phrase identification work have earlier been presented by Tjong Kim Sang

(2000b).

train  SP  DP NP
IOB1  90.68 91.59 92.02
IOB2  90.77 91.65 91.94
IOE1  90.94 91.60 91.90
IOE2  91.21 91.97 91.99
O+C  91.57 91.97 91.51
Majority  91.96 92.34 92.62
TotPrecision  91.97 92.34 92.62
TagPrecision  91.98 92.34 92.62
Precision-Recall 91.96 92.34 92.62
TagPair  92.08 92.34 92.65
MBL  92.32 92.35 92.75
MBL+  92.40 92.32 92.72

Table 4: F_{β=1} rates obtained for the three processing strategies, Single-Phase Approach (SP), Double-Phase Approach (DP) and N-Phase approach (NP), when applied to the training data of the CoNLL-2000 shared task (arbitrary chunking) while using five different data representations and seven system combination techniques. In all cases, system combination led to performances that were better than the individual system results. The computationally-intensive N-Phase Approach does better than the other two.

The best result was generated with the N-Phase Approach in combination with a stacked memory-based classifier (MBL, 92.76). A bootstrap resampling test with 8000 random populations generated the 90% significance interval 92.60-92.90 which means that this result is significantly better than any Single-Phase or Double-Phase result. However, the N-Phase approach has a big computing overhead: the number of passes over the data is at least N times the number of representations. Therefore, we have chosen the Double-Phase Approach combined with Majority Voting for our further work. This approach combines a reasonable performance with computational efficiency. The Single-Phase Approach is potentially faster but its performance is worse unless we use a stacked classifier which requires extra combinator training data.

When we applied the Double-Phase Approach combined with Majority Voting to the CoNLL-2000 data sets, we obtained an F_{β=1} rate of 92.50 (precision 94.04% and recall 91.00%). An overview of the performance rates of the different chunk types can be found in Table 5. Our system does well for the three most frequently occurring chunk types, noun phrases, prepositional phrases and verb phrases, and less well for the other seven. The chunk type UCP which occurred in the training data, was not present in the test data. With this result, our memory-based arbitrary chunker finished third of eleven participants in the CoNLL-2000 shared task. The two systems that performed better were Support Vector Machines (Kudoh and Matsumoto, 2000, F_{β=1}=93.48) and Weighted Probability Distribution Voting (Van Halteren, 2000, F_{β=1}=93.32).

test data precision recall F_{β=1}
ADJP  85.25% 59.36% 69.99
ADVP  85.03% 71.48% 77.67
CONJP 42.86% 33.33% 37.50
INTJ  100.00% 50.00% 66.67
LST  0.00% 0.00% 0.00
NP  94.14% 92.34% 93.23
PP  96.45% 96.59% 96.52
PRT  79.49% 58.49% 67.39
SBAR  89.81% 72.52% 80.25
VP  93.97% 91.35% 92.64
all  94.04% 91.00% 92.50

Table 5: The results per chunk type of processing the test data with the Double Pass Approach and Majority Voting. Although the data is formatted differently than the noun phrase chunking data, the NP F_{β=1} rate here (93.23) is close to that of our NP chunking F_{β=1} rate (93.34).

4. Parsing

In this section we will examine the application of memory-based shallow parsing to generating embedded structures. We will examine three tasks: clause identification, noun phrase parsing and full parsing. Whenever possible, we will use the methods that we have applied to chunking in the previous section.

4.1 Clause Identification

In clause identification the goal is to divide sentences in clauses which typically contain a subject and a predicate. We have used the clause data of the CoNLL-2001 shared task (Tjong Kim Sang and Déjean, 2001) which was derived from the Wall Street Journal Part of the Penn Treebank (Marcus et al., 1993). Here is an example sentence from the Treebank, with all information but words and clause brackets omitted:

(S Coach them in (S–NOM handling complaints) (SBAR–PRP so that (S they can resolve problems immediately) ) . )

This sentence contains four clauses. In the data that we have worked with, the function and type information has been removed. This means that the type tags NOM and PRP have been omitted and that the SBAR tag has been replaced by S. Like the chunking data, these data sets contained words and part-of-speech tags which were generated by the Brill tagger (Brill, 1994). Additionally they contained chunk tags which were computed by the arbitrary chunking method we discussed in the previous section.

We have approached identifying clauses in the following way:^{7} first we evaluated different memory-based learners for predicting the positions of open clause brackets and close clause brackets, regardless of their level of embedding. The two resulting bracket streams will be inconsistent and in order to solve this we have developed a list of rules which change a possibly inconsistent set of brackets to a balanced structure. The evaluation of the learners and the development of the balancing rules will be done with 10-fold cross-validation of the CoNLL-2001 training data. Information leaking is prevented by using corpus clause tags as context features in the training data of cascaded learners rather than clause tags computed in a previous learning phase. The best learner configurations and balancing rules found will be applied to the data for the clause identification shared task.

Like in our noun phrase chunking work, we have tested memory-based learners with different sets of features. At the time we performed these experiments, we did not have access to feature selection methods and therefore we have only evaluated a few fixed feature configurations:

1. words only (w)

2. POS tags only (p)

3. chunk tags only (c)

4. words and POS tags (wp)

5. words and chunk tags (wc)

6. POS tags and clause tags (pc)

7. words, POS tags and chunk tags (wpc)

All feature groups were tested with four context sizes: no context information or information about a symmetrical window of one, two or three words. Like in our chunking work, we want to check if an improved performance can be obtained by using system combination. However, since we attempt to predict brackets at all levels in one step, we cannot use the five data representations here. Instead we have evaluated combination of some of the feature configurations mentioned above: a majority vote of the three using a single type of information (1+2+3), a majority vote of the three using pairs of information (4+5+6) and a majority vote of the previous two and the one using three types of information (7+(1+2+3)+(4+5+6)). The last one is a combination of three results of which two themselves are combinations of three results.

Clauses may contain many words and it is possible that the maximal context used by the learner, three words left and right, is not enough for predicting clause boundaries accurately. However, we cannot make the context size much larger than three because that would make it harder for the learner to generalize. We have tried to deal with this problem by evaluating another set of features which contain summaries of sentences rather than every word. Since we have chunk information of the sentences available, we can compress them by removing all words from each chunk except the main one, the head word. The head

7. This approach and the results achieved with it have earlier been discussed by Tjong Kim Sang (2001a).

train  0  1  2  3  train  0  1  2  3
1 w  61.77 84.40 83.74 81.08 1 w  61.11 75.99 77.52 77.63
2 p  30.44 80.40 80.47 76.85 2 p  61.71 77.52 78.74 77.95
3 c  13.67 76.76 79.05 78.71 3 c  00.00 67.25 75.06 75.70
4 wp  62.24 87.19 84.45 81.22 4 wp  61.25 76.52 77.92 78.12
5 wc  67.95 87.31 85.74 82.97 5 wc  61.01 75.96 77.46 77.79
6 pc  49.29 86.65 84.92 81.72 6 pc  61.74 77.44 78.40 77.93
7 wpc  68.66 87.92 85.93 83.28 7 wpc  61.21 76.17 77.73 78.00
8 1+2+3 38.32 85.24 86.92 85.38 8 1+2+3 61.67 75.93 79.60 79.94
9 4+5+6 68.04 88.83 87.44 84.98 9 4+5+6 61.44 77.30 79.15 79.38
10 7+8+9 68.03 88.75 87.72 85.45 10 7+8+9 61.44 77.20 79.25 79.60
11 w-  54.05 83.70 83.48 81.25 11 w-  61.24 76.01 78.69 79.25
12 c-  14.26 77.70 79.30 78.50 12 c-  61.73 76.82 78.34 80.90
13 wc-  58.47 86.53 85.74 82.77 13 wc-  61.43 76.77 80.15 81.61

Table 6: F_{β=1} rates obtained in 10-fold cross-validation experiments with the training data while predicting open clause brackets (left) and close clause brackets (right). We used different combinations of information (w: words, p: POS tags and c: chunk tags) and different context sizes (0-3). The best results for open brackets have been obtained with a majority vote of three information pairs while using context size 1 (row 9) For close clause brackets best results were obtained with words and POS tags after compressing the chunks and while using context size 3 (row 13).

words can be generated by a set of rules put forward by Magerman (1995) and modified by Collins (1999).^{8} After removing the nonhead words from each chunk, we can replace the POS tag of the remaining word with the chunk tag and thus obtain data with words and chunk tags only (words outside of a chunk keep their POS tag). Again we have evaluated sets of features which hold a single type of information, words (w–) or chunk tags (c–), or pairs of information, words and chunk tags (wc–).

We have evaluated the twelve groups of feature sets while predicting the clause open and clause close brackets. The results can be found in Table 6. The learner performed best while predicting open clause brackets with information about the words immediately next to the current word (column 1). When more information was available, its performance dropped slightly. Of the different feature sets tested, the majority vote of sets that used pairs of information performed best (column 1, row 9). The classifiers that generated close brackets improved whenever extra context information became available. The best performance was reached while using a pair of words and chunk tags in the summarized format (column 3, row 13). We have performed an extra experiment to test if the system improved when using four context words rather than three. With words and chunk tags in the summarized format the system obtained F_{β=1}=81.72 for context size four compared with 81.61 for context size three. This increase is small so we have chosen context size three for our further experiments.

8. Available on http://www.research.att.com/˜ mcollins/papers/heads

With the streams of open and close brackets, we attempted to generate balanced clause structures by modifying the data streams with a set of heuristic rules. In these rules we gave more confidence to the open bracket predictions since, as can be seen in Table 6 the system performs better in predicting open brackets than close brackets. After testing different rule sets created by hand and evaluating these on the available training data, we decided on using the following rule set:

1. Assume that exactly one clause starts at each clause start position.

2. Assume that exactly one clause ends at each clause end position but

3. ignore all clause end positions when currently no clause is open, and

4. ignore all clause ends at non-sentence-final positions which attempt to close a clause started at the first word of the sentence.

5. If clauses are opened but not closed at the end of the sentence then close them at the penultimate word of the sentence.

These rules were able to generate complete and consistent embedded clause structures for the output that the system generated for the training data of the CoNLL-2001 shared task. The rules have one main defect: they are incapable of predicting that two or more clauses start at the same position. This will make it impossible for the system to detect such clause start but unfortunately, according to our rule set evaluation, adding recognition facilities for such multiple clause start would have a negative influence on overall performance levels. This set of rules obtained a clause F_{β=1} of 71.34 on the training data of this task when applied to the best results for open and close brackets. The rules did not change the open bracket positions and on average the changes they made to the close bracket positions were an improvement (F_{β=1} = 84.11 compared to 81.61).

An argument which could be made is that since open bracket prediction is more accurate than close bracket prediction, one could use the information of the open bracket positions when predicting clause close brackets. We have attempted to do this by repeating the experiment with the best configuration for close brackets (wc– with context size 3) while adding a feature which stated at which clause level the current word was, according to earlier open and close brackets. This approach improved the F_{β=1} rate of the close bracket predictor from 81.61 to 83.50. However, after applying the balancing rules to the open brackets and the improved close brackets, we only got a clause F_{β=1} of 71.39, a minimal improvement over the previous 71.34. It seems that the extra performance gain obtained in the close bracket predictor was obtained by solving problems which could already be solved by the balancing rules.

We applied the balancing rules together with an open bracket predictor using a combination of pairs of feature types (context size 1) and a close bracket predictor using summarized pairs of words and chunk tags (context size 3) to the data files of the CoNLL-2001 shared task. Our clause identification method obtained an F_{β=1} rate of 67.79 for identifying complete clauses (precision 76.91% and recall 60.61%). In the CoNLL-2001 shared task, the system finished third of six participants. One system outperformed the others by a large margin: the boosted decision tree method by Carreras and Màrquez (2001). Their system obtained an F_{β=1} rate of 78.63 on this task. The main difference between their approach and ours is that they use a larger number of features, methods for predicting multiple cooccurring clause starts and a more advanced statistical model for combining brackets to clauses.

In a post-conference study, we have attempted to estimate more precisely the cause of the performance difference between our method an the boosted decision trees used by Carreras and Màrquez (2001). Our hypothesis was that not only the choice of system made a difference, but also the choice of features. For this purpose, Carreras and Màrques kindly repeated an experiment in predicting open brackets but this time while using our feature set: pairs of information using a window of one word left and one right, while results were combined with majority voting (Table 6, left, row 9, column 1). The experiment was performed while testing on the CoNLL-2001 development data set. Originally the memory-based learner obtained F_{β=1} = 89.80 on this data set while their boosted decision tree approach reached 93.89. However, while using the memory-based feature set, the performance of the decision trees dropped to 91.32. When both systems use the same features, the boosted decision trees outperform the memory-based learner. But it is able to perform better with its own feature set. Our hypothesis was correct: the performance difference between the two approaches was both caused by choice of the learner and the choice of the feature set.

The next obvious question is whether the memory-based system would perform better with the feature set of the boosted decision trees. Providing an answer to this question was nontrivial. The feature set consisted of thousands of binary features which were more than the memory-based learner could handle. After converting the features from binary-valued to multi-valued, there were about 70 features left. At best, the system obtained F_{β=1} = 90.52 with this feature set. Since we feared that still the number of features was too large for the system to handle, we performed a forward sequential selection search process in the feature space starting with zero features. The memory-based learner reached an optimal performance with 13 features at F_{β=1} = 91.82. These results show that there is still room for improvement for the memory-based learner but that cooperation with a feature selection method will be helpful.

4.2 Noun Phrase Parsing

Noun phrase parsing is similar to noun phrase chunking but this time the goal is to find noun phrases at all levels. This means that just like in the clause identification task we need to be able to recognize embedded phrases. The following example sentence will illustrate this:

In ( early trading ) in ( Hong Kong ) ( Monday ) , ( gold ) was quoted at ( ( $ 366.50 ) ( an ounce ) ) .

This sentence contains seven noun phrases of which the one containing the final four words of the sentence consists of two embedded noun phrases. If we use the same approach as for clause identification, retrieving brackets of all phrase levels in one step and balancing these, we will probably not detect this noun phrase because it starts and ends together with other noun phrases. Therefore we will use a different approach here.

We will recover noun phrases at different levels by performing repeated chunking (Tjong Kim Sang, 2000a). We will start with data containing words and part-of-speech tags and identify the base noun phrases in this data with techniques used in our noun phrase chunking work. After this we will replace the phrases that were found by the head words and their tags. This will create a summary of the sentences with words and a mixed data stream of POS tags and chunk tags. We can apply our noun phrase chunking techniques to this data one more time and find noun phrases one level above the base level. The compressing and chunking steps will be repeated in order to retrieve phrases at higher levels. The process will stop when no new phrases are found.

The approach described here seems a trivial expansion of our noun phrase chunking work. However, there are some details left to discuss. First, there is the selection of the head word duing the phrase summarization process. At the time we performed these experiments, we did not have access to the Magerman/Collins set of rules for determining head words, and therefore we used a rule created by ourselves: the head word of a noun phrase is the final word of the first noun cluster in the phrase or the final word of the phrase if it does not contain a noun cluster.

The second fact we should mention, is that the data we used contains a different format of noun phrase chunks than the data we previously have worked with. In this task we use the data set which was developed for the noun phrase bracketing shared task of CoNLL-99 (Osborne, 1999). It was extracted from the Wall Street Journal part of the Penn Treebank (Marcus et al., 1993) without extra modifications and this means, for example, that possessives between two noun phrases have been attached to the first one unlike in the noun phrase chunking data. This and other differences make that we cannot be sure that the techniques we developed for the other base noun phrase format will work very well here. Indeed, there is a performance drop in the chunking part of our shallow parser when compared with the chunking work (F_{β=1} of 92.77 compared with 93.34). However, we decided not to put extra work in searching for a better configuration for our noun phrase chunker and have trained an existing chunker with the data available for this task.

An unforeseen problem occurred when we attempted to use the chunker for identifying noun phrases above the base level. Our chunker output is a majority vote of five systems using different data representations. In our evaluation work with tuning data (WSJ section 21), we observed that the overall output of the chunker at nonbase levels was worse than the performance of the best individual system (Tjong Kim Sang, 2000a). The reason for this is that the system that used the O+C data representation, outperformed the other four systems by a large margin. Because of this, and probably because the other four systems made similar errors, the errors of the four cancelled some of the correct analyses of the best system and caused the majority vote to be worse than the best individual system. For this reason we have decided to use only the bracket representations when processing noun phrases above base levels.

The main open question in this study is what training data to use when processing the nonbase noun phrases. In order to find an answer to this question we have tested several configurations while processing tuning data, WSJ section 21, with the training data for the CoNLL-99 shared task. We have tested six training data configurations for predicting open and close bracket positions: using all bracket positions, those of base phrases only, those of all phrases except base phrases, those of phrases of the current level only, those of the current level and the previous, and those of the current level and the next. At all levels, using the brackets of the current level only proved to be working best or close to best. At the sixth level no new noun phrases were detected. Therefore we decided to use only brackets of one phrase level in the training data for nonbase phrases and stop phrase identification after six levels.

We have applied a noun phrase chunker with fixed symmetrical context sizes to the noun phrase data of the CoNLL-99 shared task (Tjong Kim Sang, 2000a). The chunker generated a majority vote of open and close brackets put forward by five systems, each of which used a different representation of the base noun phrases (IOB1, IOB2, IOE1, IOE2 and O or C). All systems used a window of four left and four right for words and POS tags (18 features) and the four systems using IO representations additionally performed and extra pass with a window of three left and three right for words and POS tags, and a window of two left and two right without the focus tag for chunk tags (also 18 features). The output of the chunker was presented to a cascade of six chunkers, each of which consisted of a pair of open and close bracket predictors which were trained with brackets from one of the levels 1 to 6. After each chunk phase the phrases found were replaced by the head word of the phrase and a fixed chunk tag.

The system obtained an overall F_{β=1} rate of 83.79 (precision 90.00% and recall 78.38%) for identifying arbitrary noun phrases.^{9} It is slightly better than our performance at CoNLL-99 (82.98, obtained without system combination) which was the best of two entries submitted for the shared task at that workshop. The performance of our noun phrase chunker can be regarded as a baseline score for this data set. This score is already quite high: F_{β=1} = 79.70, and it seems that the nonbase level chunkers have not been contributing much to the performance of this shallow parser. Out of curiosity we have also examined how well a full parser does on the task of identifying arbitrary noun phrases. For this purpose we looked at output data of a parser described by Collins (1999) which was provided with the parser code (WSJ section 23, model 2). The parser obtained F_{β=1} = 89.8 (precision 89.3% and recall 90.4%) for this task. This is a lot better than our shallow parser but we should note that compared with our application, the Collins parser has access to better part-of-speech tags and more training data with more sophisticated annotation rather than only noun phrase boundaries.

4.3 Full Parsing

The approach for parsing noun phrases outlined in the previous section can be used for generating parse trees containing phrases of arbitrary phrases as well. In that case we would be using chunking techniques for performing full parsing. The is not a new idea. Ejerhed and Church (1983) present a Swedish grammar which includes noun phrase chunk rules. Abney (1991) describes a chunk parser which consists of two parts: one that finds base chunks and another that attaches the chunks to each other in order to obtain parse trees. Daelemans (1995) suggested to find long-distance dependencies with a cascade of lazy learners among which were constituent identifiers. Ratnaparkhi (1998) built a parser based on a chunker with an additional bottom-up process which determines at what position to start new phrases or to join constituents with earlier ones. With this approach he obtained state-of-the-art parsing results. Brants (1999) applied a cascade of Markov model chunkers to the task of parsing German sentences. We have extended our noun phrase parsing techniques to parsing arbitrary phrases (Tjong Kim Sang, 2001b). We will present the main findings of this study here as well.

9. This performance was already reported by Tjong Kim Sang (2000a).

The standard data sets for testing statistical parsers are different than the ones we used for our earlier work on chunking and shallow parsing. The data sets have been extracted from the Wall Street Journal (WSJ) part of the Penn Treebank (Marcus et al., 1993) as well but they contain different segments. The training data consists of sections 02-21 (39,832 sentences) while section 23 is used as test data (2416 sentences). The data sets consists of words, and part-of-speech tags which have been generated by the part-of-speech tagger described by Ratnaparkhi (1996). In the data the phrase types ADVP and PRT have been collapsed into one category and during evaluation the positions of punctuation signs in the parse tree have been ignored. These adaptations have been done by different authors in order to make it possible to compare the results of their systems with the first study that used these data sets (Magerman, 1995) and all follow-up work.

In our work on arbitrary parsing, we were interested in finding an answer to four questions. In order to obtain these answers, we have performed tests with smaller data sets which were taken from the standard training data for this task: WSJ sections 15-18 as training data and section 20 as test data. The first topic we were interested in, was the influence of context size and size of the examined nearest neighborhood size (parameter k of the memory-based learner) on the performance of the parser. We took the noun phrase parser developed in the previous section, lifted its restriction of generating noun phrases only and applied it to this data set while using different context sizes and values for parameter k for the classifiers that identified phrases above the base levels. The different types of the chunks were derived by using the Double-Phase Approach for chunking (see Section 3.3). The best configuration we found was a context of two left and two right words and POS tags with k is 1. The nearest neighborhood size is smaller than used in our earlier work (3) and the best context size is smaller than in our noun phrase chunking work (4). However, the best context size we found for this task is exactly the same as reported by Ratnaparkhi (1998).

The second topic we were interested in was the type of training data that should be used for finding phrases above the base level. In our noun phrase parsing work, we found that the best performance could be obtained by using only data of the current phrase level. This will cause a problems for our parser, since the tree depth may become as large as 31 in our corpus but there will be few training material available for these high level phrases if we use the same training configuration as in our noun phrase parsing work. We have tested two different training configurations to see if we could use more training data for this task without losing performance. With the first of these, using the current, previous and next phrase level, performance was as well (F_{β=1}=77.13) as while using only the current level (77.17). However, when we trained the cascade of chunkers while using brackets of all phrase levels, the performance dropped to 67.49. We have decided to keep on using the current phrase level only in the training data despite its problems with identifying higher level phrases.

In the results that we have presented in this paper, the precision rates have always been higher than the recall rates. For a part, this is caused by the method we use for balancing open brackets and close brackets. It removes all brackets which cannot be matched with another one which is approximately the same as accepting clauses which are likely to be correct and throwing away all others. We wanted to test if we could obtain more balanced precision and recall rates because we hoped that these would lead to a better F_{β=1} rate. Therefore we have tested two alternative methods for combining brackets. The first disregarded the type of the open brackets and allowed close brackets to be combined with open brackets of any type. The second method allowed open brackets to match with close brackets of any type. Unfortunately neither the first (F_{β=1}=72.33) nor the second method (76.06) managed to obtain the same F_{β=1} rate as our standard method for combining brackets. Therefore we decided to stick with the latter.

The final issue which we wanted to examine is the performance progression of the parser at the different levels of the process. The recall of the parser should increase for every extra step in the cascade of chunkers but we would also like to know how precision and F_{β=1} progressed. We have measured this for our small parameter tuning data set and found that indeed recall increased until level 30 of a maximum of 32 and remained constant after that. Precision dropped until the same level, remaining at the same value afterwards while F_{β=1} reached a top value at level 19 and dropped afterwards. The reason for the later drop in F_{β=1} value is that while the recall is still rising, it cannot make up for the loss of precision at later levels. Since we want to optimize the F_{β=1} rate, we have decided to restrict the number of cascaded chunkers in our parser to 19 levels. We have added an extra post-processing step which after the 19 levels of processing adds clause brackets (S) around sentences which have not already been identified as a clauses.

We have applied the best parser configuration found to the standard parsing data. Our parser used an arbitrary chunker with the configuration described in Section 3.3 (a Majority Vote of five systems using different data representations) but trained with the relevant data for this task. Higher level phrases were identified by a cascade of 19 chunkers, each of which had a pair of independent open and close bracket classifiers which used a context of two left and two right of words and POS tags while being trained with brackets of the current level only. At each level, open and close brackets were combined to chunks by removing all brackets that could not be matched with a bracket of the same type. The parser contained a post-processing process which added clause brackets around sentences which were not identified as a clause after the 19 processing stages. This chunk parser obtained an F_{β=1} rate of 80.49 on WSJ section 23 (precision 82.34% and recall 78.72%).

The performance of our chunk parser is modest compared with state-of-the-art statistical parsers, which obtain around 90 F_{β=1} rate (Collins, 1999, Charniak, 2000). However, we have a couple of suggestions for improving its performance. First, we could attempt giving the parser access to more information, for example about lower phrase levels. Currently, the parser only knows the head words and phrase types of daughters of phrases that are being built and this might not be enough. Second, we could try to find a better method for predicting bracket positions. For reasons explained in the previous section, we could not use a majority vote of systems using different representations. This might have helped to obtain a better performance. Finally, we would like to change the greedy approach of our parser. Currently it chooses the best segmentation of chunks at each level and builds on that but ideally it would be able to remember some next-to-best configurations as well and perform backtracking from the earlier choices whenever necessary. This approach would

section 20  precision recall F_{β=1}
Kudoh and Matsumoto (2001)  94.15% 94.29% 94.22
Tjong Kim Sang et al. (2000)  94.18% 93.55% 93.86
MBL  94.01% 92.67% 93.34
Tjong Kim Sang (2000a)  93.63% 92.89% 93.26
Muñoz et al. (1999)  92.4% 93.1% 92.8
Ramshaw and Marcus (1995)  91.80% 92.27% 92.03
Argamon-Engelson et al. (1999) 91.6% 91.6% 91.6
baseline  78.20% 81.87% 79.99

Table 7: A selection of results that have been published for the Ramshaw and Marcus data sets for noun phrase chunking. Our chunker (MBL) is third-best. The baseline results have been produced by a system that selects the most frequent chunk tag (IOB1) for each part-of-speech tag. The best performance for this task has been obtained by a system using Support Vector Machines (Kudoh and Matsumoto, 2001).

probably improve performance considerably (as shown by Ratnaparkhi (1998), Table 6.5). A practical problem which needs to be solved here, is that in nearest neighbor memorybased learning alternative classes do not receive confidence measures. Rather, sets of itemdependent distances are used to determine the usability of the classes. Comparing partial trees requires comparing sets of distances and it is not obvious how this should be done.

These extra measures will probably improve the performance of the chunk parser. However, it is questionable whether it is worthwhile continuing with this approach. The present version of the parser already requires a lot of memory and processing time: more than a second per word for chunking only compared with a mere 0.14 seconds per sentence for a statistical parser which performed better (Ratnaparkhi, 1998). Extra extensions will probably slow down the parser even more so we are not sure if extending this approach is worth the trouble.

5. Related Work

In this section we will compare our work with that of others that have applied machine learning techniques to the same data sets. First we will discuss the two chunking tasks and then the tasks that required output of embedded structures. Many systems have been applied to the five tasks. Rather that giving a detailed description of each of them, we will list the best performing systems for each task and mention some differences between these systems and ours. This comparison of our memory-based shallow parsers with other work shows that they produce state-of-the-art results for the chunking tasks but not for the tasks which require identification of embedded structures.

5.1 Chunking

Table 7 shows a selection of the best results published for the noun phrase chunking task.^{10} As far as we know, the results presented in this paper (line MBL) are the third-best results. We have participated in producing the second-best result (Tjong Kim Sang et al., 2000) which was produced by combining of the results of five different learning techniques. The best results for this data set have been generated with Support Vector Machines (Kudoh and Matsumoto, 2001).^{11} A statistical analysis of our current result revealed that all performances outside of the region 92.93-93.73 are significantly different from ours. This means that all results in the table, except from the 93.26, are significantly different from ours.

A topic to which we have paid little attention is the analysis of the errors that our approach makes. Such an analysis would provide insights into the weaknesses of the system and might provide clues to methods for improving the system. For noun phrase chunking we have performed a limited error analysis by manually evaluating the errors that were made in the first section of a 10-fold cross-validation experiment on the training data while using the chunker described by Tjong Kim Sang (2000a). This analysis revealed that the majority of the errors were caused by errors in the part-of-speech tags (28% of the false positives/29% of the false negatives). In order to acquire reasonable results, it is custom not to use the part-of-speech tags from the Treebank, but use tags that have been generated by a partof-speech tagger. This prevents the system performance from reaching levels which would be unattainable for texts for which no perfect part-of-speech tags exist. Unfortunately the tagger makes errors and some of these errors cause the noun phrase segmentation to become incorrect.

The second most frequently occurring error cause was related to conjunctions of noun phrases (16%/18%). Deciding whether a phrase like red dwarfs and giants consist of one or two noun phrases requires semantic knowledge and might be too ambitious for presentday systems to solve. The other major causes of errors all relate to similar hard cases: attachment of punctuation signs (15%/12%; inconsistent in the Treebank), deciding whether ambiguous phrases without conjunctions should be one or two noun phrases (11%/12%), adverb attachment (5%/4%), noun phrases containing the word to (3%/3%), Treebank noun phrase segmentation errors (3%/1%) and noun phrases consisting of the word that (0%/2%). Apart from these hard cases there also were quite a few errors for which we could not determine an obvious cause (19%/19%).

The most obvious suggestion for improvement that came out of the error analysis was to use a better part-of-speech tagger. We are currently using the Brill tagger (Brill, 1994). Better taggers are available nowadays but using the Brill tags here was necessary in order to be able to compare our approach with earlier studies, which have used the Brill tags as well. The error analysis did not produce other immediate suggestions for improving our noun phrase chunking approach. We are relieved about this because it would have been an embarrassment if our chunker had produced systematic errors. However, there is a trivial way to improve the results of the noun phrase chunker: by using more training

10. An elaborate overview of most of the systems that have been applied to this task can be found on

http://lcg-www.uia.ac.be/˜ erikt/research/np-chunking.html 11. Although we do not wish to underestimate the power of Support Vector Machines, we should note that

it seems that the optimal results presented by Kudoh and Matsumoto (2001) have been obtained by

tuning the system to the test data.

section 20  precision recall F_{β=1}
Zhang et al. (2001)  94.29% 94.01% 94.13
Kudoh and Matsumoto (2001) 93.89% 93.92% 93.91
Kudoh and Matsumoto (2000) 93.45% 93.51% 93.48
Van Halteren (2000)  93.13% 93.51% 93.32
Tjong Kim Sang (2000b)  94.04% 91.00% 92.50
Zhou et al. (2000)  91.99% 92.25% 92.12
Déjean (2000)  91.87% 92.31% 92.09
baseline  72.58% 82.14% 77.07

Table 8: A selection of results that have been published for the arbitrary chunking data set of the CoNLL-2000 shared task. Our chunker (Tjong Kim Sang, 2000b) is fifthbest. The baseline results have been produced by a system that selects the most frequent chunk tag (IOB1) for each part-of-speech tag. The best performance for this task has been obtained by a system using regularized Winnow (Zhang et al., 2001). Systems that have been applied both to the arbitrary chunking task and the noun phrase chunking task performed approximately equally well for NP chunks in both tasks.

data. Different studies have shown that by increasing the training data size by 300%, the F_{β=1} error might drop with as much as 25% (Ramshaw and Marcus, 1995, Tjong Kim Sang, 2000a, Kudoh and Matsumoto, 2001). Another study for a different problem, confusion set disambiguation, has shown that a further cut in the error rate is possible with even larger training data sets (Banko and Brill, 2001). In order to test this for noun phrase chunking we need a hand-parsed corpus which is larger than anything that is presently available.

Table 8 contains a selection of the best results published for the arbitrary chunking data used in the CoNLL-2000 shared task.^{12} Our chunker (Tjong Kim Sang, 2000b) is the fifth-best on this list. Immediately obvious is the imbalance between precision and recall: the system identifies a small number of phrases with a high precision rate. We assume that this is primarily caused by our method for generating balanced structures from streams of open and close brackets. We have performed a bootstrap resampling test on the chunk tag sequence associated with this result. An evaluation of 10,000 pairs indicated that the significance interval for our system (F_{β=1} = 92.50) is 92.18-92.81 which means that all systems that are ahead of ours perform significantly better and all systems that are behind perform significantly worse. We are not sure what is causing these large performance differences. At this moment we assume that our approach has difficulty with classification tasks when the number of different output classes increases.

5.2 Parsing

A complete overview of the clause identification results of the CoNLL-2001 shared task can be found in Table 9 (Tjong Kim Sang and Déjean, 2001). Our approach was third-best.

12. More results for the chunking task can be found on http://lcg-www.uia.ac.be/conll2000/chunking/

section 21  precision recall F_{β=1}
Carreras and Màrquez (2001) 84.82% 73.28% 78.63
Molina and Pla (2001)  70.89% 65.57% 68.12
Tjong Kim Sang (2001a)  76.91% 60.61% 67.79
Patrick and Goyal (2001)  73.75% 60.00% 66.17
Déjean (2001)  72.56% 54.55% 62.77
Hammerton (2001)  55.81% 45.99% 50.42
baseline  98.44% 31.48% 47.71

Table 9: Results of the clause identification part of the CoNLL-2001 shared task. Our clause identifier (Tjong Kim Sang, 2001a) is third-best. The baseline results have been produced by a system that only puts clause brackets around complete sentences. The best performance for this task has been obtained by a system using boosted decision trees (Carreras and Màrquez, 2001).

A bootstrap resampling test with a population of 10,000 random samples generated from our results produced the 90% significance interval 66.66-68.95 for our system which means that our result is not significantly different from the second result. The boosted decision trees used by Carreras and Màrquez (2001) did a lot better than the other systems. In Section 4.1, we have made a comparison between the performance of this system and ours and concluded that the performance differences were both caused by the choice of learning system and by a difference in the features chosen for representing the task.

The noun phrase parsing task has not received much attention in the research community and there are only few results to compare with. Osborne (1999) used a grammar-extension method based on Minimal Description Length and applied it to a Definite Clause Grammar. His system used different training and test segments of the Penn Treebank than we did. At best, it obtained an F_{β=1} rate of 60.0 on the test data (precision 53.2% and recall 68.7%). Krymolowski and Dagan (2000) applied a memory-based learning technique specialized for learning sequences to a noun phrase parsing task. Their system obtained F_{β=1}=83.7 (precision 88.5% and recall 79.3%) on yet another segment of the Treebank. This performance is very close to that of our approach (F_{β=1}=83.79). The memory-based sequence learner used much more training data than ours (about four times as much) but unlike our method, it generated its output without using lexical information, which is impressive. The performance of the Collins parser on the subtask of noun phrase parsing which we mentioned in Section 4.2 (F_{β=1}=89.8) shows that there is room for improvement left for all systems that were discussed here.^{13}

A selection of results for parsing the Penn Treebank can be found in Table 10. The F_{β=1} error rate of the best systems is about half of that of ours. A more detailed comparison of the output data of our memory-based parser and one of the versions of the Collins parser (Collins, 1999, model 2) has shown the large performance difference is caused by the way nonbase phrases are processed (Tjong Kim Sang, 2001b). Our chunker performs

13. Our full parser, which was trained and tested on the same data as the Collins parser, obtained F_{β=1}=86.96

for recognizing NP phrases only.

section 23  precision recall F_{β=1}
Collins (2000)  89.9% 89.6% 89.7
Bod (2001)  89.7% 89.7% 89.7
Charniak (2000)  89.5% 89.6% 89.5
Collins (1999)  88.3% 88.1% 88.2
Ratnaparkhi (1998)  87.5% 86.3% 86.9
Charniak (1997)  86.6% 86.7% 86.6
Magerman (1995)  84.3% 84.0% 84.1
Tjong Kim Sang (2001b) 82.3% 78.7% 80.5

Table 10: A selection of results that have been published for parsing sentences shorter than 100 words of the Penn Treebank. The performance of our parser (Tjong Kim Sang, 2001b) is not quite state-of-the-art. The best performance for this task has been obtained by statistical parsers and data-oriented parsers (Collins, 2000, Charniak, 2000, Bod, 2000).

reasonably well compared with the first stage of the Collins parser (F_{β=1} = 49.30 compared with 49.85). Especially at the first few levels after the base levels, our parser looses F_{β=1} points compared with the Collins parser. The initial difference of 0.65 at the base level grows to 2.92 after three more levels, 5.16 after six and 6.13 after nine levels with a final difference of 6.59 after 20 levels (Tjong Kim Sang, 2001b). At the end of Section 4.3, we have put forward some suggestions for improving our parser. However, we have also noted that further improvement might not be worthwhile because it will make our parser even slower than it already is.

6. Concluding Remarks

We have presented memory-based approaches to shallow parsing and we have applied these to five tasks: noun phrase chunking, arbitrary chunking, clause identification, noun phrase parsing and full parsing. We have used two additional techniques for improving the performance of our shallow parsers: feature selection and system combination. The first was used to compensate for a problem of the memory-based learner: it has difficulty with ignoring features that are not immediately relevant. While feature selection worked well in one study (clause identification with large feature sets), it did not make much difference to the overall performance of our noun phrase chunker. We believe that other techniques that were incorporated in the chunker (cascading and system combination) have already stretched the performance of the system to its limits. Therefore there might not have been much left to gain by using feature selection. System combination has proved to be quite useful for generating base phrases. Unfortunately, we could not apply it for higher level chunks because our method for producing different system results, using different data representations, failed to produce results for higher level phrases that could be improved with the Majority Voting technique we used for chunking.

A comparison of our work with other studies revealed that our approach works well for base phrase identification, but not for finding embedded structures. We have made a couple of suggestions for improving the performance on tasks that require generating embedded structures: provide different features to the learners, try to find a method which allows combination of different systems when working on higher level phrases and replace the greedy phrase selection approach currently used by one that allows backtracking from earlier choices. However, while further improvement is interesting from a scientific point of view, it might not be useful from a practical point of view. Our present method is already slower than state-of-the-art full parsers and it requires more memory. Extra improvements to this approach will probably slow it down even more without guaranteeing state-of-the-art performance.

Acknowledgments

We would like to thank our colleagues of CNTS - Language Technology Group, University of Antwerp, Belgium and ILK, University of Tilburg, The Netherlands, the members of the TMR-LCG network, in particular James Hammerton, and two anonymous reviewers for valuable discussions and comments. We are grateful to Xavier Carreras for his cooperation in the comparison study of his clause identification system with ours. This study was funded by the European Training and Mobility of Researchers (TMR) network Learning Computational Grammars.^{14}

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