Journal of Machine Learning Research 2 (2002) 595-613 Submitted 9/01; Published 3/02

Shallow Parsing using Specialized HMMs

Antonio Molina amolina@dsic.upv.es Ferran Pla fpla@dsic.upv.es Departament de Sistemes Informàtics i Computació Universitat Politècnica de València Camí de Vera s/n, 46020 València (Spain)

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

Abstract

We present a unified technique to solve different shallow parsing tasks as a tagging problem using a Hidden Markov Model-based approach (HMM). This technique consists of the incorporation of the relevant information for each task into the models. To do this, the training corpus is transformed to take into account this information. In this way, no change is necessary for either the training or tagging process, so it allows for the use of a standard HMM approach. Taking into account this information, we construct a Specialized HMM which gives more complete contextual models. We have tested our system on chunking and clause identification tasks using different specialization criteria. The results obtained are in line with the results reported for most of the relevant state-of-the-art approaches.

Keywords: Shallow Parsing, Text Chunking, Clause Identification, Statistical Language Modeling, Specialized HMMs.

1. Introduction

Shallow parsing has become an interesting alternative to full parsing. The main goal of a shallow parser is to divide a text into segments which correspond to certain syntactic units. Although the detailed information from a full parse is lost, shallow parsing can be done on non-restricted texts in an efficient and reliable way. In addition, partial syntactical information can help to solve many natural language processing tasks, such as information extraction, text summarization, machine translation and spoken language understanding.

Shallow parsing involves several different tasks, such as text chunking, noun phrase chunking or clause identification. Text chunking consists of dividing an input text into nonoverlapping segments. These segments are non-recursive, that is, they cannot include other segments and are usually called chunks as defined by Abney (1991). Noun phrase chunking (NP chunking) is a part of the text chunking task, which consists of detecting only noun phrase chunks. The aim of the Clause identification task is to detect the start and the end boundaries of each clause (sequence of words that contains a subject and a predicate) in a sentence. For example, the sentence “You will start to see shows where viewers program the program” would be chunked as follows:

\begin{matrix}c \\ ◯ \end{matrix}2002 Antonio Molina and Ferran Pla.

Molina and Pla

(NP You) (VP will start to see) (NP shows) (ADVP where) (NP viewers)

(VP program) (NP the program) .^{1}

The clauses in the sentence would be:

(S You will start to see shows (S where (S viewers program the program )) .)

Chunks and clause information in a sentence can also be represented by means of tags. In Tjong Kim Sang et al. (2000), there are several equivalent chunk tag sets for representing chunking. The IOB2 set, which was previously used by Ratnaparkhi (1998), uses three kinds of tags: B-X for the first word of a chunk of type X; I-X for a non-initial word in an X chunk; O for a word outside of any chunk. For clause identification, each word can be tagged with the corresponding brackets if the word starts and/or ends a clause, or with a null tag if the word is not the start or the end of a clause. The above example can be represented using this notation as follows:

You  B-NP  (S*
’ll  B-VP  *
start  I-VP  *
to  I-VP  *
see  I-VP  *
shows  B-NP  *
where  B-ADVP (S*
viewers B-NP  (S*
program B-VP  *
the B-NP *
program I-NP  *S)S)
.  O  *S)

Earlier approaches to solving this problem consisted of parsers which are based on a grammar of hand-coded rules. Abney (1996) developed the incremental partial parser CASS based on finite state methods for detecting chunks and clauses. Aït-Mokhtar and Chanod (1997) also built an incremental architecture of finite-state transducers that identifies chunks and detects subjects and objects. Voutilainen (1993) used a different formalism (constraint grammars) to detect NPs.

In the literature, you can find different learning methods which have been applied to perform shallow parsing: Transformation-based Learning, Memory-based Learning, Hidden Markov Models, Maximum Entropy, Support Vector Machines, etc. The first works focused mainly on NP detection. Ramshaw and Marcus (1995) used Transformation-based learning and put forward a standard training and testing data set, that has later been used to contrast other approaches. Memory-based learning was used by Daelemans et al. (1999) and Argamon et al. (1998). The main reference for text chunking is the shared task for CoNLL-2000^{2} (Tjong Kim Sang and Buchholz, 2000). In Section 4, we will briefly review the different approaches to text chunking presented in this shared task.

Shallow Parsing using Specialized HMMs

Learning approaches for clause identification have recently been developed. Orasan (2000) applied memory-based learning techniques and corrected the output by applying some rules. In the shared task for CoNLL-2001^{3} (Tjong Kim Sang and Déjean, 2001) other approaches were presented (Hidden Markov Models, Memory-based Learning, Boosting, etc.)

1. In the example NP is Noun Phrase, VP is Verbal Phrase, ADVP is Adverbial Phrase and S is a clause.

2. Chunking shared task is available at http://lcg-www.uia.ac.be/conll2000/chunking/

In this paper, we present a unified technique to construct Specialized HMMs to be used for solving shallow parsing tasks. First, in Section 2, we formalize shallow parsing as a tagging problem and define the method for the specialization of the models. In Section 3, we present the results of the application of our approach to text chunking. We test different specialization criteria under the same experimental conditions of the CoNLL-2000 shared task. We achieved state-of-the-art performance, as we show in Section 4. We also apply this technique to solve the clause identification problem in Section 5. Finally, we present some concluding remarks.

2. Shallow parsing as HMM-based tagging

We consider shallow parsing to be a tagging problem. From the statistical point of view, tagging can be solved as a maximization problem.

Let O be a set of output tags and I the input vocabulary of the application. Given an input sentence I = i_{1}, …, i_{T}, where i_{j} ∈ I : ∀j, the process consists of finding the sequence of states of maximum probability on the model. That is, the sequence of output tags, O = o_{1}, …, o_{T}, where o_{j} ∈ O : ∀j. This process can be formalized as follows:

Ô = arg max_{O} P (O|I)
= arg max_{O} (\frac{P (O) · P (I|O)}{P (I)}); O ∈ O^{T}  (1)

Due to the fact that this maximization process is independent of the input sequence, and taking into account the Markov assumptions, the problem is reduced to solving the following equation (for a second–order HMM):

arg max_{o_{1}...o_{T}} ( ∏_{j:1...T}P (o_{j}|o_{j−1}, o_{j−2}) · P (i_{j}|o_{j}))  (2)

The parameters of equation 2 can be represented as a second-order HMM whose states correspond to a tag pair. Contextual probabilities, P (o_{j}|o_{j−1}, o_{j−2}), represent the transition probabilities between states and P (i_{j}|o_{j}) represents the output probabilities.

This formalism has been widely used to efficiently solve part-of-speech (POS) tagging in (Church, 1988, Merialdo, 1994, Brants, 2000), etc. In POS tagging, the input vocabulary is composed of words and the output tags are POS or morphosyntactic tags. The segmentation produced by some different shallow parsing tasks, such as text chunking or clause identification, can be represented as a sequence of tags as we mentioned above. Therefore, these problems can also be carried out in a way similar to POS tagging. However, to successfully solve each task, it is necessary to deal with certain peculiarities.

On the one hand, you have to decide which available input information is really relevant to the task. POS tagging considers only words in the input. In contrast, chunking can

3. Clause identification shared task is available at http://lcg-www.uia.ac.be/conll2001/clauses/

T  ⟶^{f_{s}}  T̃
I  O  Ĩ  Õ
You  PRP  B-NP  PRP  PRP·B-NP
will  MD  B-VP  MD  MD·B-VP
start  VB  I-VP  VB  VB·I-VP
to  TO  I-VP  TO  TO·I-VP
see  VB  I-VP  VB  VB·I-VP
shows  NNS  B-NP  NNS  NNS·B-NP
where  WRB  B-ADVP  where·WRB  where·WRB·B-ADVP
viewers NNS  B-NP  NNS  NNS·B-NP
program VBP  B-VP  VBP  VBP·B-VP
the  DT  B-NP  DT  DT·B-NP
program NN  I-NP  NN  NN·I-NP
.  .  O  .  .·O

Figure 1: Example of the result of applying specialization on a sentence.

consider words and POS tags, and clause identification can take into account words, POS tags and chunk tags. In this case, if all this input information is considered, the input vocabulary of the application could become very large, and the model would be poorly estimated.

On the other hand, the output tag set could be too generic to produce accurate models. The contextual model can be enriched by considering a more fine-grained output tag set by adding some kind of information to the output tags. For instance, in the chunking task we have enriched the chunk tags by adding to them POS information and selected words as we will show below. This aspect has also been tackled in POS tagging (Kim et al., 1999, Lee et al., 2000, Pla and Molina, 2001), by lexicalizing the models, that is, by incorporating words into the contextual model.

In this work, we propose a simple technique that permits us to encode the available information into the model, without changing the learning or the tagging processes. This method consists of modifying the original training data set in order to consider only the relevant input information and to extend the output tags with additional information.

This transformation is the result of applying a specialization function f_{s} on the original training set T to produce a new one T̃, that is:

f_{s} : T ⊂ (I × O)^{∗} → T̃ ⊂ (Ĩ × Õ)^{∗}

This function transforms every training tuple ⟨i_{j}, o_{j}⟩ to a new tuple ⟨ĩ_{j}, õ_{j}⟩, and thus the original input and output sets to the new sets Ĩ and Õ, by concatenating the selected information. This function has to be experimentally defined for each task, as we will discuss in Section 3.

Figure 1 shows an example of the application of this function on a sample of the training set used in the chunking task. In this example, we have considered POS tags and certain selected words as relevant input information. The output tags have also been enriched with this information. For example, the tuple ⟨You·PRP, B-NP⟩ is transformed to the new tuple ⟨PRP, PRP·B-NP⟩, considering only POS information. On the other hand, the tuple ⟨where·WRB, B-ADVP⟩, considering also lexical information, is transformed to the new tuple ⟨where·WRB, where·WRB·B-ADVP⟩.

From this new training set T̃, we can learn the Specialized HMM by maximum likelihood in the usual way. The tagging process is carried out by Dynamic Programming Decoding using the Viterbi algorithm. This decoding process is not modified, you simply consider the decisions taken into account in the specialization process. That is, to consider the relevant information as input and to map the sequence of output tags (which belongs to Õ) to the original output tags (which belongs to O). This can be carried out in a direct way.

3. Chunking evaluation

We present a set of experiments in order to evaluate the chunking approach proposed in this work. We focus on the different specialization criteria considered to construct the specialized HMM. As we mentioned above, one of the advantages of our approach is that no change is needed for either the training or the decoding processes carried out when specialized HMMs are used. To confirm this, all the experimental work was conducted using the TnT^{4} tagger developed by Brants (2000) without making any modification to its source code.

TnT is a very efficient statistical POS tagger based on HMMs. To deal with sparse problems, it uses linear interpolation as a smoothing technique to estimate the model. To handle unknown words, it uses a probabilistic method based on the analysis of the suffix of the words.^{5} All the following experiments were done with TnT’s default options using second-order HMMs.

We used the data defined in the shared task of CoNLL-2000. The characteristics of this task were described by Tjong Kim Sang and Buchholz (2000). It used the same Wall Street Journal corpus sections defined by Ramshaw and Marcus (1995), that is, sections 15-18 for training, and section 20 for testing. The set of chunks (NP, VP, ADVP, ADJP, PP, SBAR, CONJP, PRT, INTJ, LST, UCP) was derived from the full parsing taking into account certain assumptions and simplifications.^{6} The POS tagging was obtained using the Brill tagger (Brill, 1995) without correcting the tagger output. We also compared our results to those obtained by the other approaches that participated in the shared task (see Section 4).

As we stated in Section 2, two decisions must be made in order to define a specialization function on the training set: which information from the available input is relevant and how to refine the output tags.

These decisions were tested experimentally. We used a development set (which was different from the test set) in order to select this information. We divided the original training set into two partitions: 90% for training and 10% for tuning (development set). To do that, we used nine consecutive sentences from the original training set for training and we used the tenth sentence for testing. We tested different combinations of input and output information on the development set and we selected those that improved the results obtained with no specialized training data set.

4. TnT is available at http://www.coli.uni-sb.de/thorsten/tnt 5. In our case, the suffix method is not the most suitable for handling unknown words because the input

vocabulary is reduced to POS tags or concatenations of POS tags and words. In all the experiments

performed, the vocabulary was always seen in the training set. Therefore, we did not consider necessary

to study this problem.

6. The script to derive the chunks is available at http://ilk.kub.nl/~sabine/chunklink/

The baseline system, which we called BASIC, considered training tuples such as ⟨p_{i}, ch_{i}⟩, that is, only POS tags (p_{i}) were taken into account as input vocabulary, and no changes were made in the chunk tag set (ch_{i}). This criterion gave a very poor performance, because the output tag set was too generic to construct accurate models. Therefore, we defined a specialization function f_{s} in order to extend the output vocabulary as follows:

f_{s}(⟨w_{i} · p_{i}, ch_{i}⟩) = ⟨p_{i}, p_{i} · ch_{i}⟩

This criterion, denoted as SP, makes training tuples ⟨p_{i}, p_{i} · ch_{i}⟩ in which only POS tags are considered as input and the output tags are enriched with the POS tag associated with the input words (w_{i}).

Finally, the SP criterion was tested by adding lexical information in both the input and the output. The problem was that adding all the words to the input and/or the output produced very large models and no improvements were observed. For this reason, we tested a selective lexicalization of the model. That is, only a set of certain relevant words (W_{s}) were considered in the contextual language model. In this respect, we defined the following specialization function:

f_{s−Lex}(⟨w_{i} · p_{i}, ch_{i}⟩) = \{ \begin{matrix}⟨w_{i} · p_{i}, w_{i} · p_{i} · ch_{i}⟩ if w_{i} ∈ W_{s} \\ ⟨p_{i}, p_{i} · ch_{i}⟩ if w_{i} ∉ W_{s} \end{matrix}

The main problem of this lexicalization is to define the set of words W_{s} to be considered. We defined some criteria in order to automatically extract the relevant words that improved the performance on the development set. These criteria are summarized below.

• Lex-WCC selects the words from the training set that belong to closed categories.^{7}

• Lex-WHF selects the words whose frequency in the training set was higher than a certain threshold. In order to determine which threshold maximized the performance of the model (that is, the best set of words to specialize the model), we tuned it on the development partition with word sets of different sizes. The best performance was obtained by selecting the words whose frequency was higher than 100.

• Lex-WTE selects the words whose chunk tagging error rate was higher than a certain threshold. These words were extracted from the output provided by the tagger that uses the SP model. The best threshold obtained corresponds to the words whose error frequency was higher than 2 in the development set.

• Lex-WCH selects the words that belong to certain chunks such as SBAR, PP and VP with high frequency in the training set.

• Lex-COM selects the words corresponding to a combination of Lex-WTE and Lex-WCH criteria.

specialization criteria precision recall F_{β=1}  |Õ| |W_{s}|
BASIC  84.16% 84.52% 84.34  22  0
SP  90.44% 89.96% 90.20 317  0
SP + Lex-WCC  91.99% 91.56% 91.77 830 154
SP + Lex-WHF  91.87% 92.14% 92.00 1,086 144
SP + Lex-WTE 92.22% 92.00% 92.11 592 38
SP + Lex-WCH  92.03% 92.25% 92.14 1,305 217
SP + Lex-COM  92.10% 92.35% 92.23 1,341 225

Table 1: Overall chunking results on the development data set using Specialized HMMs with different specialization criteria.

Table 1 shows the results of the tuning process on the development data set measured in terms of precision, recall and F_{β} rate. In addition, it shows the size of the output tag set (|Õ|) and the size of the selected word set (|W_{s}|). It can be observed that all the specializations considered outperformed the BASIC model. Although each lexicalization criterion used a different set of selected words with a size that ranked between 38 and 225 words, SP + Lex-WHF, SP + Lex-WTE and SP + Lex-WCH criteria achieved similar results while SP + Lex-WCC criterion achieved somewhat worse results. We obtained the best performance with the combination criterion SP + Lex-COM, with a F_{β} improvement of 9.4% with respect to the BASIC model. The improvement of the SP model with respect to the BASIC model was about 7% and the use of lexicalization criteria incremented F_{β} about 2% with respect to the SP model.

We think that these statistical criteria can be improved by means of a linguistic study to determine which words are really relevant to this disambiguation problem. The experiments conducted have given us some clues about this, because we have observed that the effect of some words on the overall result is not significant. For example, the SP + Lex-WTE criterion which only provide a small set of 38 specialized words performed better than other criteria.

Once the system was tested on the development data set, new models were learnt using the original training data set (sections 15-18) with the best specialization parameters obtained in the tuning process. Next, the system was tested on a new unseen data set (section 20). Table 2 shows that the system had a similar behaviour for both development and test data sets. The best performance was also obtained by using the SP + Lex-COM criterion, which suggests that a model that includes these specialization parameters could be successfully applied to other unseen data.

The results for precision, recall and F_{β} rate for all the chunks considered using the SP + Lex-COM criterion are summarized in Table 3. These results outperformed the F_{β} rate of BASIC and SP for each chunk. The details of this improvement are shown in Figure 2. The highest improvement was achieved for SBAR and PRT chunks. This is

7. The closed categories considered are: CC, DT, MD, POS, PP$, RP, TO, WDT, WP$, EX, IN, PDT,

PRP, WP, WRB.

specialization criteria precision recall F_{β=1}  |Õ| |W_{s}|
BASIC  84.31% 84.35% 84.33  22  0
SP  89.58% 89.55% 89.57 320  0
SP + Lex-WCC  91.50% 91.51% 91.51 846 154
SP + Lex-WHF  91.30% 91.76% 91.53 1,105 144
SP + Lex-WTE 91.65% 91.82% 91.73 601 38
SP + Lex-WCH  91.74% 92.12% 91.93 1,339 217
SP + Lex-COM  91.96% 92.41% 92.19 1,381 225

Table 2: Overall chunking results on the shared task using Specialized HMMs with different specialization criteria.

chunk precision recall F_{β=1}
ADJP  71.19% 67.12% 69.10
ADVP 79.84% 79.10% 79.47
CONJP 38.46% 55.56% 45.45
INTJ  50.00% 50.00% 50.00
NP  92.30% 92.68% 92.49
PP  96.58% 97.40% 96.99
PRT  71.43% 75.47% 73.39
SBAR  85.50% 84.86% 85.18
VP  91.73% 92.81% 92.26
all  91.96% 92.41% 92.19

Table 3: Chunking results on the test set of the shared task using Specialized HMMs with the SP + Lex-COM criterion.

because the set of selected words that we considered included words that usually appear in these chunks.

Due to the fact that the number of parameters of the models increases, these models will be better estimated if the training data set is larger. To confirm this, we conducted an experiment increasing the size of the training data set. We chose training data from sections 00 to 19 of the WSJ corpus; the test data set was again section 20 and we used the same word set of the shared task (|W_{s}| = 225) obtained using the SP + Lex-COM criterion to specialize the model. Figure 3 shows that F_{β} improves as the size of the training set increases, achieving a F_{β} rate of 93.25 with a training data set size of about 950,000 words and 1,960 output tags.

In Table 4 the results for each chunk using this large training data set are summarized. It can also be observed that all the F_{β} rates outperformed the results achieved with the small training data set. Although the overall F_{β} improvement is only about 1%, the best Figure 2: Improvement on F_{β} rate for certain chunks on the shared task using Specialized

HMMs.

Figure 3: Evolution of the F_{β} rate using training sets of different size.

improvements were achieved for those chunks that include selected words (6.8% for PRT, 7.9% for ADJP and 3.8% for SBAR).

Finally, we would like to note that the efficiency of the system is not reduced even if |Õ| increases when specialized models are used. For the model learnt with the SP + Lex-COM criterion the tagging speed holds around 30,000 words/second running on a Pentium

chunk precision recall F_{β=1}
ADJP  78.54% 71.00% 74.58
ADVP 81.85% 79.68% 80.75
CONJP 40.00% 66.67% 50.00
INTJ  50.00% 50.00% 50.00
NP  93.52% 93.43% 93.48
PP  96.77% 97.73% 97.25
PRT  75.00% 82.08% 78.38
SBAR  88.70% 88.04% 88.37
VP  93.33% 93.73% 93.53
all  93.25% 93.24% 93.25

Table 4: Chunking results, on the test of the shared task with large training data set, using Specialized HMMs with SP + Lex-COM criterion.

500 Mhz. Although we have not made a comparative study of the efficiency with other approaches, we think that these performances are difficult to overcome by other systems.

4. Comparison with other chunking approaches

We compared our results with those systems that have presented results for the same training and test data used in the chunking shared task performed in CoNLL-2000. Each of these systems uses a specific learning method and different kinds of information. Following, we will briefly review each of these approaches. Finally, we compare the different kinds of information used for the different learning methods. Other comparisons, such as an efficiency comparison, cannot be done because the authors did not usually report it. Basically, these systems can be divided into the following groups: Rule-based systems, Memory-based systems, Statistical systems and Combined Systems.

Rule-based systems

The ALLiS system (Déjean, 2000) is based on theory refinement. It attempts to improve a previously learnt grammar using “contextualization” and “lexicalization” operators. The method only takes context into account if the confidence value for a certain POS tag is under a certain threshold. It calculates the ratio between the number of occurrences of a POS tag in a chunk and the number of occurrences of this POS tag in the training corpora. If this ratio is higher than a certain threshold, then the corresponding chunk tag is assigned. If not, it takes into account left and right context (“contextualization”) and the current word (“lexicalization”).

The system presented by Johansson (2000) picks the most likely chunk tag for a given context, assuming that a larger context (if it has been seen in the training data) overrides the label proposed by a smaller context. It obtains the best results for 5-context, that is, a context of the current POS tag, the two left POS tags and the two right POS tags.

Memory-based systems

Veenstra and Van den Bosch (2000) studied how the memory-based learning algorithm, implemented in TiMBL software, performs with different settings. Memory-based learning consists of storing the instances seen during learning in memory along with the corresponding categories. A new instance can be classified in a category by computing the distance between the new instance and the stored instances. The best results are obtained using IB1-IG algorithm (Daelemans et al., 1997) and applying the modified value difference metric to POS features.

Statistical systems

Osborne (2000) used Ratnaparkhi’s Maximum Entropy-based tagger (Ratnaparkhi, 1996) to perform chunking. To do that, the input to the tagger is redefined to be a concatenation (“configuration”) of the different contexts that are useful for chunking. It performs chunking in two steps. First, it guesses a default chunk tag by applying a model which has been learnt with configurations which consists of current POS tags and words. Second, it produces the definitive chunk tags by applying a model learnt with configurations which also takes into account the chunk tags previously guessed. Little improvement is achieved by incorporating suffix and prefix information to the configurations.

The approach of Koeling (2000) also builds a ME model which takes into account several individual features and complex features combining POS tags and chunk tags.

In our previous work (Pla et al., 2000b), we used a two level first-order HMM that performed tagging and chunking at the same time. The model was also refined by lexicalization to improve its performance.

Zhou, Su, and Tey (2000) incorporated contextual information into a bigram model by means of defining structured tags as input. These tags are composed of the current word (if it belongs to a certain category), the current POS tag, the previous POS tag, the descriptor of the phrase category and a structural relation tag that indicates if two adjacent words have the same parent. The model is refined by an error-learning technique that keeps only the words whose error rate decreases when they are incorporated into the model. Finally, a memory-based sequence learning is applied to incorporate chunk pattern probabilities achieving slight improvement.

Combined systems

These systems combine the output of different classifiers in order to improve the chunking performance.

Kudo and Matsumoto (2000) combined several Support Vector Machine classifiers. SVMs are a very suitable learning approach for solving two-class pattern recognition problems. Basically, SVMs are binary classifiers that can guess whether an instance (a token) belongs to a class or not. Pairwise classification is used to solve chunking (which is a multiclass task). This consists of training a classifier for each pair of different chunk tags (for K chunk tags, K*(K-1)/2 classifiers have to be learnt). The results of all the classifiers are combined by a dynamic programming algorithm. Later Kudo and Matsumoto (2001)

System  Method  w w_{left} w_{right} p p_{left} p_{right} c_{left} F_{β}
[KM01]  SVM(Comb)  x  2  2  x 2  2  2 93.91
[ZDJ01]  Winnow  x  2  2  x 2  2  2 93.51
[Hal00]  WPDV(Comb) x 1-5  1-5 x 3-5  3  2 93.32
[LR01]  Winnow  93.02
[TKS00]  MBL(Comb)  x  4  4  x 4  4  92.50
SP+Lex-COM HMM  x  2  x 2  2 92.19
[ZST00]  HMM + MBL x  1  x 1  1 92.12
[Dej00]  Rule-based  x  x 1  1  92.09
[Koe00]  ME  x  1  1  x 3  2  3 91.97
[Osb00]  ME  x  2  2  x 2  2  2 91.94
[VB00]  MBL  x  5  3  x 5  3  91.54
[PMP00]  HMM  x  1  x 1  1 90.14
[Joh00]  Rule-based  x 0-3  0-3  87.23

Table 5: F_{β} results of the different shallow parsing systems related and a comparison of the information used by them in the learning process.

introduced a weighted voting technique that improved the results on the same training and test data.

By majority voting, Tjong Kim Sang (2000) combined the results provided by five different memory-based learning classifiers (one classifier for each different chunk representation, that is, IOB1, IOB2, IOE1, IOE2 and C+O). In all cases, the combined system outperformed the individual systems. The system performs the task in two steps. First, it identifies chunk boundaries and second, it assigns the chunk tag. In the first step it considers POS and words as features and, in the second step, it adds the context of chunk tags guessed in the first phase.

Van Halteren (2000) presented another combined system that uses a more sophisticated combining technique called Weighted Probability Distribution Voting (WPDV). It combines the output of five classifiers: a memory-based learning classifier and four different WPDV classifiers. Due to the fact that output can present some systematic errors, these are also corrected using a WPDV model for each kind of error.

Recently, other systems based on the Winnow algorithm have been applied to chunking on the same data set. These systems learn different classifiers. Each predicts the start or the end of a kind of chunk. The output of these classifiers is combined in order to chunk the sentence satisfying some constraints such as non-overlapping constraints. The SNoW architecture, which is based on the Winnow algorithm, is used by Li and Roth (2001). The algorithm was modified by Zhang, Damerau, and Johnson (2001) to guarantee its convergence for linearly non-separable data and was successfully applied to text chunking.

Comparison

In Table 5 we have summarized the features that each approach takes into account and the F_{β} result reported. The table indicates whether a model uses some of the following features: the current word (w), current POS tag (p), the words to the left (w_{left}) and to the right (w_{right}), the POS tags to the left (p_{left}) and to the right (p_{right}), and the chunk tag (c_{left}) to the left. In addition, Osborne (2000) considers prefix and suffix word information, and the current chunk; Koeling (2000) also incorporates complex features by concatenating individual features; Zhou et al. (2000) include structural relations between words, the descriptor of the phrase category, and takes into account only certain words; Tjong Kim Sang (2000) also considers a context of two left and two right chunk tags guessed in a first level. Zhang et al. (2001) also include second-order features, such as POS-POS, chunk-chunk, word-POS and chunk-POS. Li and Roth (2001) do not report feature information.

It can be seen that combined systems perform better than the individual systems (only Winnow-based systems outperform some of the combined systems). There are six systems that produce very similar results (between 91.5% and 92.2%). The results of our system (Specialized HMMs) are slightly better than these individual classifiers. Only the Winnowbased systems perform better than Specialized HMMs. One conclusion that we can draw from Table 5 is that HMM approaches perform better than other systems and require encoding less information. This shows the importance not only of the feature selection but the importance of algorithm itself. It can be considered that the dynamic programming decoding algorithm used by HMM systems to solve the maximization equation implicitly takes into account information of the whole sentence.

We have found two approaches that use a technique similar to the one we describe in this paper: the Osborne (2000) Maximum Entropy approach and the HMM used by Zhou, Su, and Tey (2000). Like Specialized HMMs, both approaches consider structural tags or concatenations as input to the system. The differences lie in the underlying model used and the information which is taken into account in the input. Both approaches need to encode more information than Specialized HMMs, but they do not achieve better results: Osborne achieved a similar result (F_{β}=91.94), but Zhou et al. obtained lower results (F_{β}=89.57) when error correcting and memory-based techniques were not applied. Moreover, Osborne needed to learn two models that have to be applied sequentially: a first model that proposes an initial chunk tag, and a second one that takes into account the information provided by the first one.

Note that the performance of our preliminary system (Pla et al., 2000b) was very poor because it used bigram models instead of trigram models. In addition, it only took words as input in order to perform tagging and chunking at the same time, which decreased the performance as we reported in (Pla et al., 2000a).

5. Clause identification

Clause identification can be carried out in a way similar to text chunking, using the specialization technique previously presented. As in text chunking, the success of the method lies in the definition of the specialization function, that is, in an appropriate selection of the input information and the output tag set.

All the following experiments were conducted under the same conditions defined at the clause-splitting shared task of CoNLL-2001 (Tjong Kim Sang and Déjean, 2001). At this shared task, clause detection was divided into three parts: clause start-boundary detection, clause end-boundary detection and embedded clause detection. Here, we only report results

T  ⟶^{f_{s}}  T̃
I  O  Ĩ  Õ
You  PRP B-NP  (S*  PRP·B-NP  PRP·(S1*
will  MD B-VP  *  MD·B-VP  MD·*1
start  VB  I-VP  *  VB·I-VP  VB·*1
to  TO  I-VP  *  TO·I-VP  TO·*1
see  VB  I-VP  *  VB·I-VP  VB·*1
shows  NNS B-NP  *  NNS·B-NP  NNS·*1
where  WRB B-ADVP  (S*  WRB·B-ADVP  WRB·(S2*
viewers NNS B-NP  (S*  NNS·B-NP  NNS·(S3*
program VBP B-VP  *  VBP·B-VP  VBP·*3
the  DT  B-NP  *  DT·B-NP  DT·*3
program NN  I-NP  *S)S)  NN·NN  NN·*S3)S2)
.  .  O  *S)  .·O  O·*1S)

Figure 4: Example of the result of applying specialization on a sample of the training set used in clause-splitting.

on the third part of the task (the rest of the results can be consulted in (Molina and Pla, 2001)). This shared task defined sections 15-18 from the WSJ corpus as training data set, section 20 as development set and section 21 as test set.

Clause-splitting can be seen as a step after chunking. Therefore, the available input information consists of words, POS tags and chunk tags. The output information are the clause tags. We performed clause-splitting in two phases: first, we found the best segmentation in clauses for the input sentence using a Specialized HMM; second, we corrected some balancing inconsistencies observed in the output by applying some rules.

In this case, we considered that the relevant input information for clause-detection was formed by POS tags and chunk tags. In order to produce more accurate models, the output tags were enriched with the POS tag. To avoid incorrectly balanced clauses in the output, we also added the number corresponding to the depth level of the clause in the sentence to the output tags. Thus, the specialization function f_{s} was defined as follows:

f_{s}(⟨w_{i} · p_{i} · ch_{i}, s_{i}⟩) = ⟨p_{i} · ch_{i}, p_{i} · si^{′}⟩

where s_{i} is a clause tag and si^{′} is the enumerated clause tag (each bracket in the clause tag is enumerated with the depth level of the corresponding clause). The effect of the application of this function (SP criterion) on a sentence can be seen in Figure 4.

Due to the fact that models have to be smoothed to guarantee a complete coverage of the language, this does not assure the correct balancing of the output. Therefore, we applied the following correcting rules to repair the inconsistencies in the output:

1. If the clause segmentation presents more start than end boundaries, we add the end boundaries that are needed to the last word in the sentence (just before the dot).

system  precision recall F_{β=1}
Carreras and Màrquez 84.82% 73.28% 78.63
SP+Lex-WHF  70.89% 65.57% 68.12
Tjong Kim Sang  76.91% 60.61% 67.79
SP  69.62% 64.17% 66.79
Patrick and Goyal  73.75% 60.00% 66.17
Déjean  72.56% 54.55% 62.77
Hammerton  55.81% 45.99% 50.42

Table 6: Clause-splitting results of the different systems presented in the shared task of CoNLL-2001.

2. If the clause segmentation presents more end than start boundaries, we add the start boundaries that are needed to the first word in the sentence.

3. If the sentence does not start with a start boundary or does not finish with an end boundary, we add these start and end tags.

An alternative solution would be to incorporate these rules into the model, but to do this it would be necessary to modify learning, decoding and smoothing processes, which is far from this preliminary approach to clause-detection.

Finally, we also tested the different specialization criteria obtaining slight improvements. The best results were achieved by incorporating some of the most frequent words into the model (SP+Lex-WHF criterion). These results, which can be seen in Table 6, are in line with those presented in the clause-splitting shared task of CoNLL-2001 by other systems (Tjong Kim Sang and Déjean, 2001). Our system achieved a performance which was slightly better than others based on boosting algorithms, neuronal networks, symbolic methods or memory-based learning. The best system used Ada-Boost learning combined with decision trees. In addition, the set of features was adapted to the task following linguistic criteria.

6. Concluding remarks

In this work, we have presented a technique that allows us to tackle different natural language disambiguation tasks as tagging problems. In particular, we have addressed the shallow parsing and the clause identification problems. Using this technique, the relevant information for each task can be determined. Thus, a specific task can be performed using a standard HMM-based tagger without modifying the learning and testing processes.

The results reported here show that the HMM approach performs in line with other approaches that use more sophisticated learning methods when an appropriate definition of the input and output vocabularies is provided. Moreover, this approach maintains the efficiency of the system throughout both the learning and the testing phases.

The specialization methods proposed are independent of the corpus and the language used. The lexicalization criteria presented provide sets of words that are very common, such as words that belong to closed categories or words that appear frequently in the corpus.

These selected words can also appear in other English corpora and, therefore, the chunking or clause identification problem could be successfully solved using this technique. Moreover, the criteria presented here are independent of the language. This has been contrasted in previous works for the POS tagging problem in English and Spanish corpora (Pla and Molina, 2001, Pla et al., 2001). Unfortunately, these aspects could not been tested for chunking and clause-detection due to the unavailability of other annotated corpora.

We think the method presented here can be improved in two aspects: the selection of the features that have to be included in the input and output vocabularies for each disambiguation task, and the selection of the words that really improve the performance of the system. To do this, it would be necessary to take into account not only statistical criteria, but linguistic criteria as well.

Finally, due to the fact that this technique does not need to change the learning and tagging processes, we think that the application of this technique using other taggers based on different paradigms could be of interest.

Acknowledgments

We would like to thank to the reviewers for their helpful comments. This work has been supported by the Spanish research projects CICYT TIC2000–0664–C02–01 and TIC2000– 1599–C01–01.

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