Modeling Commonality among Related Classes in Relation Extraction

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 121–128, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Modeling Commonality among Related Classes in Relation Extraction Zhou GuoDong Su Jian Zhang Min Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613 Email: {zhougd, sujian, mzhang}@i2r.a-star.edu.sg Abstract This paper proposes a novel hierarchical learn- ing strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually prede- fined or automatically clustered, a linear dis- criminative function is determined in a top- down way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector. As the upper-level class normally has much more positive train- ing examples than the lower-level class, the corresponding linear discriminative function can be determined more reliably. The upper- level discriminative function then can effec- tively guide the discriminative function learn- ing in the lower-level, which otherwise might suffer from limited training data. Evaluation on the ACE RDC 2003 corpus shows that the hierarchical strategy much improves the per- formance by 5.6 and 5.1 in F-measure on least- and medium- frequent relations respec- tively. It also shows that our system outper- forms the previous best-reported system by 2.7 in F-measure on the 24 subtypes using the same feature set. 1 Introduction With the dramatic increase in the amount of tex- tual information available in digital archives and the WWW, there has been growing interest in techniques for automatically extracting informa- tion from text. Information Extraction (IE) is such a technology that IE systems are expected to identify relevant information (usually of pre- defined types) from text documents in a certain domain and put them in a structured format. According to the scope of the ACE program (ACE 2000-2005), current research in IE has three main objectives: Entity Detection and Tracking (EDT), Relation Detection and Characterization (RDC), and Event Detection and Characterization (EDC). This paper will focus on the ACE RDC task, which detects and classifies various semantic relations between two entities. For example, we want to determine whether a person is at a location, based on the evidence in the context. Extraction of semantic relationships between entities can be very useful for applications such as question answering, e.g. to answer the query “Who is the president of the United States?”. One major challenge in relation extraction is due to the data sparseness problem (Zhou et al 2005). As the largest annotated corpus in relation extraction, the ACE RDC 2003 corpus shows that different subtypes/types of relations are much unevenly distributed and a few relation subtypes, such as the subtype “Founder” under the type “ROLE”, suffers from a small amount of annotated data. Further experimentation in this paper (please see Figure 2) shows that most rela- tion subtypes suffer from the lack of the training data and fail to achieve steady performance given the current corpus size. Given the relative …

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