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<Paper uid="N01-1025">
  <Title>Chunking with Support Vector Machines</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Chunking is recognized as series of processes -first identifying proper chunks from a sequence of tokens (such as words), and second classifying these chunks into some grammatical classes. Various NLP tasks can be seen as a chunking task. Examples include English base noun phrase identification (base NP chunking), English base phrase identification (chunking), Japanese chunk (bunsetsu) identification and named entity extraction. Tokenization and part-of-speech tagging can also be regarded as a chunking task, if we assume each character as a token.</Paragraph>
    <Paragraph position="1"> Machine learning techniques are often applied to chunking, since the task is formulated as estimating an identifying function from the information (features) available in the surrounding context. Various machine learning approaches have been proposed for chunking (Ramshaw and Marcus, 1995; Tjong Kim Sang, 2000a; Tjong Kim Sang et al., 2000; Tjong Kim Sang, 2000b; Sassano and Utsuro, 2000; van Halteren, 2000).</Paragraph>
    <Paragraph position="2"> Conventional machine learning techniques, such as Hidden Markov Model (HMM) and Maximum Entropy Model (ME), normally require a careful feature selection in order to achieve high accuracy. They do not provide a method for automatic selection of given feature sets. Usually, heuristics are used for selecting effective features and their combinations. null New statistical learning techniques such as Support Vector Machines (SVMs) (Cortes and Vapnik, 1995; Vapnik, 1998) and Boosting(Freund and Schapire, 1996) have been proposed. These techniques take a strategy that maximizes the margin between critical samples and the separating hyperplane. In particular, SVMs achieve high generalization even with training data of a very high dimension. Furthermore, by introducing the Kernel function, SVMs handle non-linear feature spaces, and carry out the training considering combinations of more than one feature.</Paragraph>
    <Paragraph position="3"> In the field of natural language processing, SVMs are applied to text categorization and syntactic dependency structure analysis, and are reported to have achieved higher accuracy than previous approaches.(Joachims, 1998; Taira and Haruno, 1999; Kudo and Matsumoto, 2000a).</Paragraph>
    <Paragraph position="4"> In this paper, we apply Support Vector Machines to the chunking task. In addition, in order to achieve higher accuracy, we apply weighted voting of 8 SVM-based systems which are trained using distinct chunk representations. For the weighted voting systems, we introduce a new type of weighting strategy which are derived from the theoretical basis of the SVMs.</Paragraph>
  </Section>
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