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<Paper uid="J01-4005">
  <Title>An Algorithm for Anaphora Resolution in Spanish Texts</Title>
  <Section position="8" start_page="563" end_page="564" type="concl">
    <SectionTitle>
6. Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, we have presented an algorithm for identifying noun phrase antecedents of third person personal pronouns, demonstrative pronouns, reflexive pronouns, and  Palomar et al. Anaphora Resolution in Spanish Texts omitted pronouns in Spanish. The algorithm is applied to the syntactic structure generated by the slot unification parser--see Ferrdndez, Palomar, and Moreno (1998a, 1998b, 1999)--and coordinates different kinds of knowledge (lexical, morphological, and syntactic) by distinguishing between constraints and preferences.</Paragraph>
    <Paragraph position="1"> The main contribution of this paper is the introduction of an algorithm for anaphora resolution for Spanish. In our work, we have undertaken an exhaustive study of the importance of each kind of knowledge in anaphora resolution for Spanish. Moreover, we have developed a definition of syntactic conditions of NP-pronoun noncoreference in Spanish with partial parsing. We have also adapted our anaphora resolution algorithm to the problem of partial syntactic knowledge, that is to say, when partial parsing of the text is accomplished.</Paragraph>
    <Paragraph position="2"> For unrestricted texts, our approach is somewhat less accurate, since semantic information is not taken into account. For such texts, we are dealing with the output of a POS tagger, which does not provide this sort of knowledge. In order to test our approach with texts of different genres by different authors, we have worked with two different Spanish corpora, literary texts (the Lexesp corpus) and technical texts (the Blue Book), containing a total of 1,677 pronoun occurrences.</Paragraph>
    <Paragraph position="3"> The algorithm successfully identified the antecedent of the pronoun for 76.8% of these pronoun occurrences. Other algorithms usually work with different kinds of knowledge, different texts, and different languages. In order to make a more valid comparison of our algorithm with others, we adapted the other algorithms so that they would operate using only partial-parsing knowledge. In this evaluation, our algorithm has always obtained better results.</Paragraph>
    <Paragraph position="4"> Moreover, based on the results on our study of the importance of each kind of knowledge, we can emphasize that constraints are very important for resolving anaphora successfully, since they considerably reduce the number of possible candidates. null In future studies, we will attempt to evaluate the importance of semantic information in unrestricted texts for anaphora resolution in Spanish texts (Saiz-Noeda, Su~rez, and Peral 1999). This information will be obtained from a lexical tool (e.g., Spanish WordNet), which can be automatically consulted (since the tagger does not provide this information).</Paragraph>
  </Section>
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