Combining Outputs of Multiple Japanese Named Entity Chunkers by Stacking

Combining Outputs of Multiple Japanese Named Entity Chunkers by Stacking Takehito Utsuro Department of Information and Computer Sciences, Toyohashi University of Technology Tenpaku-cho, Toyohashi 441-8580, Japan utsuro@ics.tut.ac.jp Manabu Sassano Fujitsu Laboratories, Ltd. 4-4-1, Kamikodanaka, Nakahara-ku, Kawasaki 211-8588, Japan sassano@jp.fujitsu.com Kiyotaka Uchimoto Keihanna Human Info-Communications Research Center, Communications Research Laboratory Hikaridai Seika-cho, Kyoto 619-0289, Japan uchimoto@crl.go.jp Abstract In this paper, we propose a method for learning a classifier which combines out- puts of more than one Japanese named entity extractors. The proposed combi- nation method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of sev- eral classifiers at the first stage by learn- ing a second stage classifier to combine those outputs at the first stage. Individ- ual models to be combined are based on maximum entropy models, one of which always considers surrounding contexts of a fixed length, while the other consid- ers those of variable lengths according to the number of constituent morphemes of named entities. As an algorithm for learn- ing the second stage classifier, we employ a decision list learning method. Experi- mental evaluation shows that the proposed method achieves improvement over the best known results with Japanese named entity extractors based on maximum en- tropy models. 1 Introduction In the recent corpus-based NLP research, sys- tem combination techniques have been successfully applied to several tasks such as parts-of-speech tagging (van Halteren et al., 1998), base noun phrase chunking (Tjong Kim Sang, 2000), and pars- ing (Henderson and Brill, 1999; Henderson and Brill, 2000). The aim of system combination is to combine portions of the individual systems’ outputs which are partial but can be regarded as highly ac- curate. The process of system combination can be decomposed into the following two sub-processes: 1. Collect systems which behave as differently as possible: it would help a lot if at least the col- lected systems tend to make errors of differ- ent types, because simple voting technique can identify correct outputs. Previously studied techniques for collecting such systems include: i) using several exist- ing real systems (van Halteren et al., 1998; Brill and Wu, 1998; Henderson and Brill, 1999; Tjong Kim Sang, 2000), ii) bagging/boosting techniques (Henderson and Brill, 1999; Hen- derson and Brill, 2000), and iii) switching the data expression and obtaining several mod- els (Tjong Kim Sang, 2000). 2. Combine the outputs of the several systems: previously studied techniques include: i) vot- ing techniques (van Halteren et al., 1998; Tjong Kim Sang, 2000; Henderson and Brill, 1999; Henderson and Brill, 2000), ii) switch- ing among several systems according to con- fidence values they provide (Henderson and Brill, 1999), iii) stacking techniques (Wolpert, 1992) which train a second stage classifier for Association for Computational Linguistics. Language Processing (EMNLP), Philadelphia, July 2002, pp. 281-288. Proceedings of the Conference on Empirical Methods in Natural combining outputs of classifiers at the first stage (van Halteren et al., 1998; Brill and Wu, 1998; Tjong Kim Sang, 2000). In this paper, we propose a method …

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