Inducing Gazetteers for Named Entity Recognition by Large-Scale Clustering of Dependency Relations

Proceedings of ACL-08: HLT, pages 407–415, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Inducing Gazetteers for Named Entity Recognition by Large-scale Clustering of Dependency Relations Jun’ichi Kazama Japan Advanced Institute of Science and Technology (JAIST), Asahidai 1-1, Nomi, Ishikawa, 923-1292 Japan kazama@jaist.ac.jp Kentaro Torisawa National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0289 Japan torisawa@nict.go.jp Abstract We propose using large-scale clustering of de- pendency relations between verbs and multi- word nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since depen- dency relations capture the semantics of MNs well, the MN clusters constructed by using dependency relations should serve as a good gazetteer. However, the high level of computa- tional cost has prevented the use of clustering for constructing gazetteers. We parallelized a clustering algorithm based on expectation- maximization (EM) and thus enabled the con- struction of large-scale MN clusters. We demonstrated with the IREX dataset for the Japanese NER that using the constructed clus- ters as a gazetteer (cluster gazetteer) is a effec- tive way of improving the accuracy of NER. Moreover, we demonstrate that the combina- tion of the cluster gazetteer and a gazetteer ex- tracted from Wikipedia, which is also useful for NER, can further improve the accuracy in several cases. 1 Introduction Gazetteers, or entity dictionaries, are important for performing named entity recognition (NER) accu- rately. Since building and maintaining high-quality gazetteers by hand is very expensive, many meth- ods have been proposed for automatic extraction of gazetteers from texts (Riloff and Jones, 1999; The- len and Riloff, 2002; Etzioni et al., 2005; Shinzato et al., 2006; Talukdar et al., 2006; Nadeau et al., 2006). Most studies using gazetteers for NER are based on the assumption that a gazetteer is a mapping from a multi-word noun (MN)1 to named en- tity categories such as “Tokyo Stock Exchange → {ORGANIZATION}”.2 However, since the corre- spondence between the labels and the NE categories can be learned by tagging models, a gazetteer will be useful as long as it returns consistent labels even if those returned are not the NE categories. By chang- ing the perspective in such a way, we can explore more broad classes of gazetteers. For example, we can use automatically extracted hyponymy relations (Hearst, 1992; Shinzato and Torisawa, 2004), or au- tomatically induced MN clusters (Rooth et al., 1999; Torisawa, 2001). For instance, Kazama and Torisawa (2007) used the hyponymy relations extracted from Wikipedia for the English NER, and reported improved accu- racies with such a gazetteer. We focused on the automatically induced clus- ters of multi-word nouns (MNs) as the source of gazetteers. We call the constructed gazetteers clus- ter gazetteers. In the context of tagging, there are several studies that utilized word clusters to prevent the data sparseness problem (Kazama et al., 2001; Miller et al., 2004). However, these methods cannot produce the MN clusters required for constructing gazetteers. In addition, the clustering methods used, such as HMMs and Brown’s algorithm (Brown et …

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