Paper Info
Title
A Comparison of Statistical and Rule-Induction Learners for Automatic Tagging of Time Expressions in English
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.
Year
DOI
Venue
2007
10.1109/TIME.2007.38
Alicante
Keywords
Field
DocType
learning (artificial intelligence),natural language processing,statistical analysis,text analysis,English,automatic tagging,grammars,handmade extraction rules,natural language processing,rule-induction learners,statistical learners,temporal information,text understanding,time expression recognition,time expression representation,time expressions,time information
Rule-based machine translation,Automatic summarization,Question answering,Normalization (statistics),Expression (mathematics),Computer science,Information extraction,Natural language processing,Chunking (psychology),Artificial intelligence,Rule induction
Conference
ISSN
ISBN
Citations 
1530-1311
978-0-7695-2836-6
9
PageRank 
References 
Authors
0.84
8
3
Name
Order
Citations
PageRank
Jordi Poveda190.84
Mihai Surdeanu22582174.69
Jordi Turmo330630.52