Semantic Role Labeling Using Support Vector Machines

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 197–200, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Semantic Role Labeling Using Support Vector Machines Tomohiro Mitsumori � , Masaki Murata � , Yasushi Fukuda � Kouichi Doi � , and Hirohumi Doi � � Graduate School of Information Science, Nara Institute of Science and Technology 8916-5, Takayama-cho, Ikoma-shi, Nara, 630-0101, Japan � mitsumor,doy � @is.naist.jp, doi@cl-sciences.co.jp � National Institute of Information and Communications Technology 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan murata@nict.go.jp � Sony-Kihara Research Center Inc. 1-14-10 Higashigotanda, Shinagawa-ku, Tokyo, 141-0022, Japan yasu@krc.sony.co.jp Abstract In this paper, we describe our systems for the CoNLL-2005 shared task. The aim of the task is semantic role labeling using a machine-learning algorithm. We apply the Support Vector Machines to the task. We added new features based on full parses and manually categorized words. We also report on system performance and what effect the newly added features had. 1 Introduction The CoNLL-2005 shared task (Carreras and M`arquez, 2005) concerns the recognition of au- tomatic semantic roles for the English language. Given a sentence, the task consists of analyzing the propositions expressed by various target verbs of the sentence. The semantic roles of constituents of the sentence are extracted for each target verb. There are semantic arguments such as Agent, Patient, and Instrument and also adjuncts such as Locative and Temporal. We performed the semantic role labeling using Support Vector Machines (SVMs). Systems that used SVMs achieved good performance in the CoNLL-2004 shared task, and we added data on full parses to it. We prepared a feature used by the full parses, and we also categorized words that appeared in the training set and added them as features. Here, we report on systems for automatically labeling se- mantic roles in a closed challenge in the CoNLL- 2005 shared task. This paper is arranged as follows. Section 2 de- scribes the SVMs. Our system is described Sec- tion 3, where we also describe methods of data rep- resentation, feature coding, and the parameters of SVMs. The experimental results and conclusion are presented in Sections 4 and 5. 2 Support Vector Machines SVMs are one of the binary classifiers based on the maximum margin strategy introduced by Vap- nik (Vapnik, 1995). This algorithm has achieved good performance in many classification tasks, e.g. named entity recognition and document classifica- tion. There are some advantages to SVMs in that (i) they have high generalization performance inde- pendent of the dimensions of the feature vectors and (ii) learning with a combination of multiple features is possible by using the polynomial kernel func- tion (Yamada and Matsumoto, 2003). SVMs were used in the CoNLL-2004 shred task and achieved good performance (Hacioglu et al., 2004) (Kyung- Mi Park and Rim, 2004). We used YamCha (Yet Another Multipurpose Chunk Annotator) 1 (Kudo and Matsumoto, 2001), which is a general purpose SVM-based chunker. We also used TinySVM2 as a package for SVMs. 3 System Description 3.1 Data Representation …

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