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<Paper uid="W00-0732">
  <Title>Improving Chunking by Means of Lexical-Contextual Information in Statistical Language Models</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In this work, we present a stochastic approach to shallow parsing. Most of the current approaches to shallow parsing have a common characteristic: they take the sequence of lexical tags proposed by a POS tagger as input for the chunking process. Our system produces tagging and chunking in a single process using an Integrated Language Model (ILM) formalized as Markov Models. This model integrates several knowledge sources: lexical probabilities, a contextual Language Model (LM) for every chunk, and a contextual LM for the sentences.</Paragraph>
    <Paragraph position="1"> We have extended the ILM by adding lexical information to the contextual LMs. We have applied this approach to the CoNLL-2000 shared task improving the performance of tile chunker.</Paragraph>
  </Section>
class="xml-element"></Paper>