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<Paper uid="N04-2004">
  <Title>A Computational Framework for Non-Lexicalist Semantics</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The understanding of natural language text includes not only analysis of syntactic structure, but also of semantic content. Due to advances in statistical syntactic parsing techniques (Collins, 1997; Charniak, 2001), attention has recently shifted towards the harder question of analyzing the meaning of natural language sentences.</Paragraph>
    <Paragraph position="1"> A common lexical semantic representation in the computational linguistics literature is a frame-based model where syntactic arguments are associated with various semantic roles (essentially frame slots). Verbs are viewed as simple predicates over their arguments. This approach has its roots in Fillmore's Case Grammar (1968), and serves as the foundation for two current large-scale semantic annotation projects: FrameNet (Baker et al., 1998) and PropBank (Kingsbury et al., 2002).</Paragraph>
    <Paragraph position="2"> Underlying the semantic roles approach is a lexicalist assumption, that is, each verb's lexical entry completely encodes (more formally, projects) its syntactic and semantic structures. Alternations in argument structure are usually attributed to multiple lexical entries (i.e., verb senses). Under the lexicalist approach, the semantics of the verb break might look something like this: (1) break(agent, theme) agent: subject theme: object break(agent, theme, instrument) agent: subject theme: object instrument: oblique(with) break(theme) theme: subject . . .</Paragraph>
    <Paragraph position="3"> The lexicon explicitly specifies the different subcategorization frames of a verb, e.g., the causative frame, the causative instrumental frame, the inchoative frame, etc. The major drawback of this approach, however, is the tremendous amount of redundancy in the lexicon--for example, the class of prototypical transitive verbs where the agent appears as the subject and the theme as the direct object must all duplicate this pattern.</Paragraph>
    <Paragraph position="4"> The typical solution to the redundancy problem is to group verbs according to their argument realization patterns (Levin, 1993), possibly arranged in an inheritance hierarchy. The argument structure and syntax-tosemantics mapping would then only need to be specified once for each verb class. In addition, lexical rules could be formulated to derive certain alternations from more basic forms.</Paragraph>
    <Paragraph position="5"> Nevertheless, the lexicalist approach does not capture productive morphological processes that pervade natural language, for example, flat.V ! flatten.ADJ or hammer.N ! hammer.V; most frameworks for computational semantics fail to capture the deeper derivational relationship between morphologically-related terms. For languages with rich derivational morphology, this problem is often critical: the standard architectural view of morphological analysis as a preprocessor presents difficulties in handling semantically meaningful affixes.</Paragraph>
    <Paragraph position="6"> In this paper, I present a computational implementation of Distributed Morphology (Halle and Marantz, 1993), a non-lexicalist linguistic theory that erases the distinction between syntactic derivation and morphological derivation. This framework leads to finer-grained semantics capable of better capturing linguistic generalizations.</Paragraph>
  </Section>
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