Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 528–535, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns Xiaofeng Yang Jian Su Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore, 119613 {xiaofengy,sujian}@i2r.a-star.edu.sg Abstract Semantic relatedness is a very important fac- tor for the coreference resolution task. To obtain this semantic information, corpus- based approaches commonly leverage pat- terns that can express a specific semantic relation. The patterns, however, are de- signed manually and thus are not necessar- ily the most effective ones in terms of ac- curacy and breadth. To deal with this prob- lem, in this paper we propose an approach that can automatically find the effective pat- terns for coreference resolution. We explore how to automatically discover and evaluate patterns, and how to exploit the patterns to obtain the semantic relatedness information. The evaluation on ACE data set shows that the pattern based semantic information is helpful for coreference resolution. 1 Introduction Semantic relatedness is a very important factor for coreference resolution, as noun phrases used to re- fer to the same entity should have a certain semantic relation. To obtain this semantic information, previ- ous work on reference resolution usually leverages a semantic lexicon like WordNet (Vieira and Poe- sio, 2000; Harabagiu et al., 2001; Soon et al., 2001; Ng and Cardie, 2002). However, the drawback of WordNet is that many expressions (especially for proper names), word senses and semantic relations are not available from the database (Vieira and Poe- sio, 2000). In recent years, increasing interest has been seen in mining semantic relations from large text corpora. One common solution is to utilize a pattern that can represent a specific semantic rela- tion (e.g., “X such as Y” for is-a relation, and “X and other Y” for other-relation). Instantiated with two given noun phrases, the pattern is searched in a large corpus and the occurrence number is used as a measure of their semantic relatedness (Markert et al., 2003; Modjeska et al., 2003; Poesio et al., 2004). However, in the previous pattern based ap- proaches, the selection of the patterns to represent a specific semantic relation is done in an ad hoc way, usually by linguistic intuition. The manually se- lected patterns, nevertheless, are not necessarily the most effective ones for coreference resolution from the following two concerns: • Accuracy. Can the patterns (e.g., “X such as Y”) find as many NP pairs of the specific se- mantic relation (e.g. is-a) as possible, with a high precision? • Breadth. Can the patterns cover a wide variety of semantic relations, not just is-a, by which coreference relationship is realized? For ex- ample, in some annotation schemes like ACE, “Beijing:China” are coreferential as the capital and the country could be used to represent the government. The pattern for the common “is- a” relation will fail to identify the NP pairs of such a “capital-country” relation. To deal with this problem, …

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