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Lexical Selection and Paraphrase in a Meaning-Text Generation Model

  • Lidija Iordanskaja
  • Richard Kittredge
  • Alain Polguère
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 119)

Abstract

We introduce a computationally tractable model for language generation based on the Meaning-Text Theory of Mel’cuk et al., in which the lexicon plays a central role. To illustrate the descriptive scope and paraphrase capabilities of the model, we show how the lexicon influences the set of choices at four different points during the multi-stage realization process: (1) semantic net simplification, (2) determination of root lexical node for the deep syntactic dependency tree, (3) possible application of deep paraphrase rules using lexical functions, and (4) surface syntactic realization. We also show some of the ways in which the theme/rheme specifications within the semantic net influence lexical and syntactic choices during realization. Examples are taken primarily from an implemented system which generates paragraph-length reports about the usage of operating systems.

Keywords

Semantic Network Lexical Entry Dependency Tree Tree Transduction Natural Language Generation 
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

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Lidija Iordanskaja
  • Richard Kittredge
  • Alain Polguère

There are no affiliations available

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