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<Paper uid="W97-0810">
  <Title>Subject and Object Dependency Extraction Using Finite-State Transducers</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Dependency extraction from large corpora is mainly used in two major directions: automatic acquisition of lexical patterns \[Brent, 1991; Grishman and Sterling, 1992; Briscoe and Carrol, 1994; Sanfilippo, 1994\] ~ or for end-user applications such as document indexing or information retrieval \[Grefenstette, 1994\].</Paragraph>
    <Paragraph position="1"> We describe and evaluate an approach for fast automatic recognition and extraction of subject and object dependency relations from large French corpora, using a sequence of finite-state transducers. The extraction is based on robust shallow parsing. We extract syntactic relations without producing complete parse trees in the  traditional sense. The extraction requires no subcategorisation information. It relies on POS information only. The extraction is performed in two major steps:  1. incremental finite-state parsing annotates the input string with syntactic markings; 2. the annotated string is transduced in order to extract subject/verb and object/verb relations.</Paragraph>
    <Paragraph position="2">  Our incremental and cautious approach during the first phase allows the system to deal successfully with complex phenomena such as embeddings, coordination of VPs and NPs or non-standard word order.</Paragraph>
    <Paragraph position="3"> We evaluated subject and object dependency extraction on various types of unrestricted corpora. Precision is around 90-97% for subjects (84-88% for objects) and recall around 86-92% for subjects (80-90% for objects). The paper also provides some error analysis; in particular, we evaluate the impact of POS tagging errors on the extraction process.</Paragraph>
  </Section>
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