An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition

An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition Kiyoshi Sudo, Satoshi Sekine, and Ralph Grishman Department of Computer Science New York University 715 Broadway, 7th Floor, New York, NY 10003 USA � sudo,sekine,grishman � @cs.nyu.edu Abstract Several approaches have been described for the automatic unsupervised acquisi- tion of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a de- pendency chain. The effect of these al- ternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary sub- trees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improve- ment in recall using Subtree patterns. 1 Introduction Information Extraction (IE) is the process of identi- fying events or actions of interest and their partici- pating entities from a text. As the field of IE has de- veloped, the focus of study has moved towards au- tomatic knowledge acquisition for information ex- traction, including domain-specific lexicons (Riloff, 1993; Riloff and Jones, 1999) and extraction pat- terns (Riloff, 1996; Yangarber et al., 2000; Sudo et al., 2001). In particular, methods have recently emerged for the acquisition of event extraction pat- terns without corpus annotation in view of the cost of manual labor for annotation. However, there has been little study of alternative representation models of extraction patterns for unsupervised acquisition. In the prior work on extraction pattern acquisition, the representation model of the patterns was based on a fixed set of pattern templates (Riloff, 1996), or predicate-argument relations, such as subject-verb, and object-verb (Yangarber et al., 2000). The model of our previous work (Sudo et al., 2001) was based on the paths from predicate nodes in dependency trees. In this paper, we discuss the limitations of prior extraction pattern representation models in relation to their ability to capture the participating entities in scenarios. We present an alternative model based on subtrees of dependency trees, so as to extract enti- ties beyond direct predicate-argument relations. An evaluation on scenario-template tasks shows that the proposed Subtree model outperforms the previous models. Section 2 describes the Subtree model for extrac- tion pattern representation. Section 3 shows the method for automatic acquisition. Section 4 gives the experimental results of the comparison to other methods and Section 5 presents an analysis of these results. Finally, Section 6 provides some concluding remarks and perspective on future research. 2 Subtree model Our research on improved representation models for extraction patterns is motivated by the limitations of the prior extraction pattern representations. In this section, we review two of the previous models in detail, namely the Predicate-Argument model (Yan- garber et al., 2000) and the Chain model (Sudo et al., 2001). The main cause of difficulty in finding entities by extraction patterns is the fact that the participating entities can appear not only as an argument of the predicate that describes …

Content truncated to 500 words