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<Paper uid="W97-0310">
  <Title>Assigning Grammatical Relations with a Back-off Model</Title>
  <Section position="3" start_page="0" end_page="90" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Assigning a parse structure to the German sentence  (1) involves addressing the fact that it is syntactically ambiguous: (1) Eine hohe Inflationsrate erwartet die ()konomin.  a high inflation rate expects the economist 'The economist expects a high inflation rate.' In this sentence it must be determined which nominal phrase is the subject of the verb. The verb erwarren ('to expect') takes, in one reading, a nominative NP as its subject and an accusative NP as its object. The nominal phrases preceding and following the verb in (1) are both ambiguous with respect to case; they may be nominative or accusative. Further, both NPs agree in number with the verb, and since in German any major constituent may be fronted in a verb-second clause, both NPs may be the subject/object of the verb. In this example, morpho-syntactical information is not sufficient to determine that the nominal phrase \[NP die C)konomin\] ('the economist') is the subject of the verb, and \[NP Eine hohe Inflationsrate\] ('a high inflation rate') its object.</Paragraph>
    <Paragraph position="1"> Determining the subject/object of an ambiguous construct such as (1) with a knowledge-based approach requires (at least) a lexical representation specifying the classes of entities which may serve as arguments in the relation(s) denoted by each verb in the vocabulary, as well as membership information with respect to these classes for all entities denoted by nouns in the vocabulary. One problem with this approach is that it is usually not available for a broad-coverage system.</Paragraph>
    <Paragraph position="2"> This paper proposes an approximation, similar to the empirical approaches to PP attachment decision (Hindle and Rooth, 1993; Ratnaparkhi, Reynar, and Roukos, 1994; Collins and Brooks, 1995). These make use of unambiguous examples provided by a treebank or a learning procedure in order to train a model to decide the attachment of ambiguous constructs. In the current setting, this approach involves learning the classes of nouns occurring unambiguously as subject/object of a verb in sample text, and using the classes thus obtained to disambiguate ambiguous constructs.</Paragraph>
    <Paragraph position="3"> Unambiguous examples are provided by sentences in which morpho-syntactical information suffices to determine the subject and object of the verb. For instance in (2), the nominal phrase \[NP der C)konom\] with a masculine head noun is unambiguously nominative, identifying it as the subject of the verb. In (3), both NPs are ambiguous with respect to case; however, the nominal phrase \[NP Die 0konomen\] with a plural head noun is the only one to agree in number with the verb, identifying it as its subject. (2) Eine hohe Inflationsrate erwartet der 0konom.</Paragraph>
    <Paragraph position="4"> a high inflation rate expects the economist 'The economist expects a high inflation rate.' (3) Die Okonomen erwarten eine hohe Inflationsrate.</Paragraph>
    <Paragraph position="5"> the economists expect a high inflation rate 'The economists expect a high inflation rate.' This paper describes a procedure to determine the subject and object in ambiguous German constructs automatically. It is based on shallow parsing techniques employed to collect training and test data from (un)ambiguous examples in a text corpus,  and the back-off model to determine which NP in a morpho-syntactically ambiguous construct is the subject/object of the verb, based on the evidence provided by the collected training data.</Paragraph>
  </Section>
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