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Machine Translation

, Volume 8, Issue 3, pp 147–173 | Cite as

What can be learned from raw texts?

An integrated tool for the acquisition of case roles, taxonomic relations and disambiguation criteria
  • Roberto Basili
  • Maria Teresa Pazienza
  • Paola Velardi
Article

Abstract

The growing availability of large on-line corpora encourages the study of word behaviour directly from accessible raw texts. However, the methods by which lexical knowledge should be extracted from plain texts is still a matter of debate and experimentation. In this paper we present an integrated tool for lexical acquisition from corpora, ARIOSTO, based on a hybrid methodology that combines typical NLP techniques, such as (shallow) syntax and semantic markers, with numerical processing. The lexical data extracted by this method, calledclustered association data, are used for a variety of interesting purposes, such as the detection of selectional restrictions, the derivation of syntactic ambiguity criteria and the acquisition of taxonomic relations.

Keywords

Artificial Intelligence Computational Linguistic Association Data Language Translation Numerical Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Roberto Basili
    • 1
  • Maria Teresa Pazienza
    • 1
  • Paola Velardi
    • 2
  1. 1.Dip. di Ingegneria ElettronicaUniversita' di Roma “Tor Vergata”Italy
  2. 2.Istituto d'InformaticaUniversita' di AnconaItaly

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