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What can be learned from raw texts?

An integrated tool for the acquisition of case roles, taxonomic relations and disambiguation criteria

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.

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Basili, R., Pazienza, M.T. & Velardi, P. What can be learned from raw texts?. Mach Translat 8, 147–173 (1993). https://doi.org/10.1007/BF00982637

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Keywords

  • Artificial Intelligence
  • Computational Linguistic
  • Association Data
  • Language Translation
  • Numerical Processing