NDLI: A Comparison of Statistical and Rule-Induction Learners for Automatic Tagging of Time Expressions in English

Content Provider	IEEE Xplore Digital Library
Author	Poveda, J. Surdeanu, M. Turmo, J.
Copyright Year	2007
Description	Author affiliation: Tech. Univ. of Catalonia, Barcelona (Poveda, J.; Surdeanu, M.; Turmo, J.)
Abstract	Proper recognition and handling of temporal information contained in a text is key to understanding the flow of events depicted in the text and their accompanying circumstances. Consequently, time expression recognition and representation of the time information they convey in a suitable normalized form is an important task relevant to several problems in Natural Language Processing. In particular, such an analysis is largely significant for Information Extraction (IE), Question Answering (QA) and Automatic Summarization (AS). The most common approach to time expression recognition in the past has been the use of handmade extraction rules (grammars), which also served as the basis for normalization. Our aim is to explore the possibilities afforded by applying machine learning techniques to the recognition of time expressions. We focus on recognizing the appearances of time expressions in text (not normalization) and transform the problem into one of chunking, where the aim is to correctly assign Begin, Inside or Outside (BIO) tags to tokens. In this paper, we explain the knowledge representation used and compare the results obtained in our experiments with two different methods, one statistical (support vector machines) and one of rule induction (FOIL). Our empirical analysis shows that SVMs are superior.
Starting Page	141
Ending Page	149
File Size	151998
Page Count	9
File Format	PDF
ISBN	9780769528366
ISSN	15301311
DOI	10.1109/TIME.2007.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-28
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tagging Data mining Support vector machines Text recognition Support vector machine classification Natural language processing Machine learning XML Information analysis Knowledge representation
Content Type	Text
Resource Type	Article

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