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<Paper uid="W06-2937">
  <Title>The Exploration of Deterministic and Efficient Dependency Parsing</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The target of dependency parsing is to automatically recognize the head-modifier relationships between words in natural language sentences. Usually, a dependency parser can construct a similar grammar tree with the dependency graph. In this year, CoNLL-X shared task (Buchholz et al., 2006) focuses on multilingual dependency parsing without taking the language-specific knowledge into account. The ultimate goal of this task is to design an ideal multilingual portable dependency parsing system.</Paragraph>
    <Paragraph position="1"> To accomplish the shared task, we present a very light-weight and efficient parsing model to the 13 distinct treebanks (Haji et al., 2004; Simov et al., 2005; Simov and Osenova, 2003; Chen et al., 2003; Bohmova et al., 2003; Kromann 2003; van der Beek et al., 2002; Brants et al., 2002; Kawata and Bartels, 2000; Afonso et al., 2002; Dzeroski et al., 2006; Civit and Marti 2002; Nivre et al., 2005; Oflazer et al., 2003; Atalay et al., 2003) with a three-step process, Nivre's algorithm (Nivre, 2003), root parser, and post-processing. Our method is quite different from the conventional three-pass processing, which usually exhaustively processes the whole dataset three times, while our method favors examining the &amp;quot;un-parsed&amp;quot; tokens, which incrementally shrink. At the beginning, we slightly modify the original parsing algorithm (proposed by (Nivre, 2003)) to construct the initial dependency graph. A root parser is then used to recognize root words, which were not parsed during the previous step. At the third phase, the post-processor (which is another learner) recognizes the still un-parsed words. However, in this paper, we aim to build a multilingual portable parsing model without employing deep language-specific knowledge, such as lemmatization, morphologic analyzer etc. Instead, we only make use of surface lexical and part-of-speech (POS) information. Combining these shallow features, our parser achieves a satisfactory result for most languages, especially Japanese.</Paragraph>
    <Paragraph position="2"> In the remainder of this paper, Section 2 describes the proposed parsing model, and Section 3 lists the experimental settings and results. Section 4 presents the discussion and analysis of our parser with three selected languages. In Section 5, we draw the future direction and conclusion.</Paragraph>
  </Section>
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