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Báo cáo khoa học: "Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns"

Semantic relatedness is a very important factor for the coreference resolution task. To obtain this semantic information, corpusbased approaches commonly leverage patterns that can express a specific semantic relation. The patterns, however, are designed manually and thus are not necessarily the most effective ones in terms of accuracy and breadth. To deal with this problem, in this paper we propose an approach that can automatically find the effective patterns for coreference resolution. We explore how to automatically discover and evaluate patterns, and how to exploit the patterns to obtain the semantic relatedness information. . | 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 factor for the coreference resolution task. To obtain this semantic information corpusbased approaches commonly leverage patterns that can express a specific semantic relation. The patterns however are designed manually and thus are not necessarily the most effective ones in terms of accuracy and breadth. To deal with this problem in this paper we propose an approach that can automatically find the effective patterns 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 refer to the same entity should have a certain semantic relation. To obtain this semantic information previous 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 528 been seen in mining semantic relations from large text corpora. One common solution is to utilize a pattern that can represent a specific semantic relation 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