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<Paper uid="P98-2201">
  <Title>A Connectionist Approach to Prepositional Phrase Attachment for Real World Texts</Title>
  <Section position="3" start_page="0" end_page="1233" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Structural ambiguity is one of the most serious problems faced by Natural Language Processing (NLP) systems. It occurs when the syntactic information does not suffice to make an assignment decision.</Paragraph>
    <Paragraph position="1"> Prepositional phrase (PP) attachment is, perhaps, the canonical case of structural ambiguity. What kind of information should we use in order to solve this ambiguity? In most cases, the information needed comes from a local context, and the attachlnent decision is based essentially on the relationships existing between predicates and arguments, what Katz y Fodor (1963) called selectional restrictions. For example, in the expression: (V accommodate) (gP Johnson's election) (PP as a director), the PP is attached to the NP. However, in the expression: (V taking) (NP that news) (PP as a sign to be cautions), the PP is attached to the verb. In both expressions, the attachment site is decided on tile basis of verb and noun seleetional restrictions.</Paragraph>
    <Paragraph position="2"> In other eases, the information determining the PP attachment comes from a global context. In this paper we will focus on the disambiguation mechanism based on selectional restrictions.</Paragraph>
    <Paragraph position="3"> Previous work has shown that it is extremely difficult to build hand-made rule-based systems able to deal with this kind of problem. Since such hand-made systems proved unsuccessful, in recent years two main methods have appeared capable of auto- null matic learning from tagged corpora: automatic rule based methods and statistical methods. In this paper we will show that, providing that the problem is correctly approached, an NN can obtain better results than any of the methods used to date for PP attachment disambiguation.</Paragraph>
    <Paragraph position="4"> Statistical methods consider how a local context can disambiguate PP attachment estimating the probability from a corpus: p(verb attachlv NP1 prep NP2) Since an NP can be arbitrarily complex, the problem can be simplified by considering that only the heads of the respective phrases are relevant when deciding PP attachment. Therefore, ambiguity is resolved by means of a model that takes into account only phrasal heads: p(verb attachlverb nl prep n2).</Paragraph>
    <Paragraph position="5"> There are two distinct methods for establishing the relationships between the verb and its arguments: methods using words (lexical preferences) and methods using semantic classes (selectional restrictions).</Paragraph>
  </Section>
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