Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 356–365, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing Manabu Sassano Yahoo Japan Corporation Midtown Tower, 9-7-1 Akasaka, Minato-ku, Tokyo 107-6211, Japan msassano@yahoo-corp.jp Sadao Kurohashi Graduate School of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan kuro@i.kyoto-u.ac.jp Abstract We investigate active learning methods for Japanese dependency parsing. We propose active learning methods of using partial dependency relations in a given sentence for parsing and evaluate their effective- ness empirically. Furthermore, we utilize syntactic constraints of Japanese to ob- tain more labeled examples from precious labeled ones that annotators give. Ex- perimental results show that our proposed methods improve considerably the learn- ing curve of Japanese dependency parsing. In order to achieve an accuracy of over 88.3%, one of our methods requires only 34.4% of labeled examples as compared to passive learning. 1 Introduction Reducing annotation cost is very important be- cause supervised learning approaches, which have been successful in natural language processing, re- quire typically a large number of labeled exam- ples. Preparing many labeled examples is time consuming and labor intensive. One of most promising approaches to this is- sue is active learning. Recently much attention has been paid to it in the field of natural language pro- cessing. Various tasks have been targeted in the research on active learning. They include word sense disambiguation, e.g., (Zhu and Hovy, 2007), POS tagging (Ringger et al., 2007), named entity recognition (Laws and Sch¨utze, 2008), word seg- mentation, e.g., (Sassano, 2002), and parsing, e.g., (Tang et al., 2002; Hwa, 2004). It is the main purpose of this study to propose methods of improving active learning for parsing by using a smaller constituent than a sentence as a unit that is selected at each iteration of active learning. Typically in active learning for parsing a sentence has been considered to be a basic unit for selection. Small constituents such as chunks have not been used in sample selection for parsing. We use Japanese dependency parsing as a target task in this study since a simple and efficient algorithm of parsing is proposed and, to our knowledge, ac- tive learning for Japanese dependency parsing has never been studied. The remainder of the paper is organized as fol- lows. Section 2 describes the basic framework of active learning which is employed in this research. Section 3 describes the syntactic characteristics of Japanese and the parsing algorithm that we use. Section 4 briefly reviews previous work on active learning for parsing and discusses several research challenges. In Section 5 we describe our proposed methods and others of active learning for Japanese dependency parsing. Section 6 describes experi- mental evaluation and discussion. Finally, in Sec- tion 7 we conclude this paper and point out some future directions. 2 Active Learning 2.1 Pool-based Active Learning Our base framework of active learning is based on the algorithm of (Lewis and …

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