<?xml version="1.0" standalone="yes"?>
<Paper uid="P93-1033">
  <Title>AN EMPIRICAL STUDY ON THEMATIC KNOWLEDGE ACQUISITION BASED ON SYNTACTIC CLUES AND HEURISTICS</Title>
  <Section position="8" start_page="73548" end_page="73548" type="relat">
    <SectionTitle>
5. RELATED WORK
</SectionTitle>
    <Paragraph position="0"> To explore the acquisition of domain-independent semantic knowledge, the universal linguistic constraints postulated by many linguistic studies may provide gefieral (and perhaps coarse-grained) hints.</Paragraph>
    <Paragraph position="1"> The hints may be integrated with domain-specific semantic bias for various applications as well. In the branch of Lhe study, GB theory (Chomsky81) and universal feature instantiation principles (Gazdar85) had been shown to be applicable in syntactic knowledge ,.cquisition (Berwick85, Liu92a, Liu92b).</Paragraph>
    <Paragraph position="2"> The proposed method is closely related to those methodolog,.es. The major difference is that, various thematic theories are selected and computationalized for thematic knowledge acquisition. The idea of structural patterns in Montemagni92 is similar to Preposition Heuristic in that the patterns suggest general guidance to information extraction.</Paragraph>
    <Paragraph position="3"> Extra information resources are needed for thematic knawledge acquisition. From the cognitive point of view, morphological, syntactic, semantic, contextual (Jacobs88), pragmatic, world knowledge, and observations of the environment (Webster89, Siskind90) .~e all important resources. However, the availability~of the resources often deteriorated the feasibility of learning from a practical standpoint.</Paragraph>
    <Paragraph position="4"> The acquisition often becomes &amp;quot;circular&amp;quot; when relying on semantic information to acquire target semantic informatmn.</Paragraph>
    <Paragraph position="5"> Prede~:ined domain linguistic knowledge is another important information for constraining the hypothesis ,space in learning (or for semantic bootstrapping). From this point of view, lexical categories (Zernik89, Zemik90) and theory of lexical semantics (Pustejovsky87a, Pustejovsky87b) played similar role~ as the clues and heuristics employed in this paper. The previous approaches had demonstrated theC/::etical interest, but their performance on large-scale acquisition was not elaborated. We feel that, requ~,ng the system to use available resources only (i.e, .,;yntactic processors and/or syntactically processed c'orpora) may make large-scale implementations more feasible. The research investigates the issue as to l what extent an acquisition system may acquire thematic knowledge when only the syntactic resources a:e available.</Paragraph>
    <Paragraph position="6"> McClelland86 showed a connectionist model for thematic role assignment. By manually encoding training ass!gnments and semantic microfeatures for a limited number of verbs and nouns, the connectionist network learned how to assign roles. Stochastic approaches (Smadja91, Sekine92) also employed available corpora to acquire collocational data for resolving ambiguities in parsing. However, they acquired numerical values by observing the whole 248, training corpus (non-incremental learning). Explanation for those numerical values is difficult to derive in those models. As far as the large-scale thematic knowledge acquisition is concerned, the incremental extensibility of the models needs to be further improved.</Paragraph>
  </Section>
class="xml-element"></Paper>