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<Paper uid="W00-1103">
  <Title>Use of Dependency Tree Structures for the Microcontext Extraction</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In several recent years, natural language processing (NLP) has brought some very interesting and promising outcomes. In the field of information retrieval (IR), however, these significant advances have not been applied in an optimal way yet.</Paragraph>
    <Paragraph position="1"> Author argues that traditional IR methods, i.e. methods based on dealing with individual terms without considering their relations, can be overcome using NLP procedures. The reason for this expectation is the fact that NLP methods are able to detect the relations among terms in sentences and that the information obtained can be stored and used for searching. Features of word senses and the significance of word contexts are analysed and possibility of searching based on word senses instead of mere words is examined.</Paragraph>
    <Paragraph position="2"> The core part of the paper focuses on analysing Czech sentences and extracting the context relations among words from them. In order to make use of lemmatisation and morphological and syntactic tagging of Czech texts, author proposes a method for construction of dependency word microcontexts fully automatically extracted from texts, and several ways how to exploit the microcontexts for the sake of increasing retrieval performance.</Paragraph>
  </Section>
class="xml-element"></Paper>