Call For Papers: Special Issue of the Journal of Machine Learning Research

Machine Learning Approaches to Shallow Parsing

Editors: James Hammerton james.hammerton@ucd.ie,
Miles Osborne osborne@cogsci.ed.ac.uk,
Susan Armstrong susan.armstrong@issco.unige.ch, and
Walter Daelemans walter.daelemans@uia.ua.ac.be

The Journal of Machine Learning Research invites authors to submit papers for the Special Issue on Machine Learning approaches to Shallow Parsing.

Background

Over the last decade there has been an increased interest in applying machine learning techniques to corpus-based natural language processing. In particular many techniques have been applied to shallow parsing of large corpora, where rather than produce a detailed syntactic or semantic analysis of each sentence, key parts of the syntactic structure or key pieces of semantic information are identified or extracted. For example, such tasks include identifying the noun phrases in a text, extracting non-overlapping chunks of text that identify the major phrases in a sentence or extracting the subject, main verb and object from a sentence.

Applications of shallow parsing include data mining from unstructured textual material (e.g. web pages, newswires), information extraction, question answering, automated annotation of linguistic corpora and the preprocessing of data for linguistic tasks such as machine translation or full scale parsing.

Shallow parsing of realistic, naturally occuring language poses a number of challenges for a machine learning system. Firstly, the training set is usually large which will push batch techniques to the limit. The training material is often noisy and frequently only partially determines a model (that is, only some aspects of the target model are observed). Secondly, shallow parsing requires making large numbers of decisions which translates as learning large models. The size of such models usually results in extremely sparse counts, which makes reliable estimation difficult. In sum, learning how to do shallow parsing will tax almost any machine learning algorithm and will thus provide valuable insight into real-world performance.

In a number of workshops and publications, a variety of machine learning techniques have been applied in this area including memory based (instance based) learning, inductive logic programming, probabilistic context free grammars, maximum entropy, transformation based learning, artificial neural networks and more recently support vector machines. However there has not been an opportunity to compare and contrast these techniques in a systematic manner. The special issue will thus provide a venue for drawing together the relevant ML techniques.

TOPICS

The aim of the special issue is to solicit and publish papers that provide a clear view of the state of the art in machine learning for shallow parsing. We therefore encourage submissions in the following areas: To facilitate cross-paper comparison and thus strengthen the special issue as a whole, authors are encouraged to consider using one of the following data sets provided via the CoNLL workshops (please note however that this is not mandatory):

http://lcg-www.uia.ac.be/conll2000/chunking/

or:

http://lcg-www.uia.ac.be/conll2001/clauses/

We emphasise that authors will not be solely judged in terms of raw performance and this is not to be considered as a competition: insight into the strengths and weaknesses of a given system is deemed to be more important.

High quality papers reviewing machine learning for shallow parsing will also be welcome.

Instructions

Articles should be submitted electronically. Postcript or PDF format are acceptable and submissions should be single column and typeset in 11 pt font format, and include all author contact information on the first page. See the author instructions at www.jmlr.org for more details.

To submit a paper send the normal emails asked for by the JMLR in their information for authors to submissions@jmlr.org (not to the editors directly), indicating in the subject headers that the submission is intended for the Special Issue on Machine Learning Approaches to Shallow Parsing.

Key dates

Submission deadline: 2nd September 2001

Notification of acceptance: 16th November 2001

Final drafts: 3rd February 2002

Further information

Please contact James Hammerton with any queries.
James Hammerton
Last modified: Wed May 2 13:06:17 EDT 2001