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Semantic Representations for NLP Using VerbNet and the Generative Lexicon.

Susan Windisch Brown1, Julia Bonn1, Ghazaleh Kazeminejad1

  • 1Department of Linguistics, University of Colorado Boulder, Boulder, CO, United States.

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Summary
This summary is machine-generated.

New semantic representations for VerbNet, inspired by linguistic theories, aim to improve natural language processing (NLP) inference capabilities. These detailed representations capture event participant changes, enhancing NLP systems' understanding of subtle linguistic interactions.

Keywords:
NLPVerbNetlexical resourcelexiconnatural language processingsemantic representationsemantics

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Lexicography

Background:

  • Current natural language processing (NLP) systems struggle with inferencing tasks due to limitations in deep semantic understanding.
  • Tasks requiring detection of subtle participant interactions and implicit event sequencing remain challenging for NLP.
  • Human language comprehension, even with sparse input, highlights the potential for linguistic insights to improve NLP.

Purpose of the Study:

  • To develop novel, hand-crafted semantic representations for the VerbNet lexical resource.
  • To integrate linguistic theories, specifically the Generative Lexicon (GL) model of subevent semantics, into VerbNet.
  • To enhance the semantic processing capabilities of NLP systems for improved inferencing.

Main Methods:

  • Drew upon linguistic theories from the Generative Lexicon (GL) for subevent semantics.
  • Developed detailed semantic representations for VerbNet verb classes, focusing on participant state changes across events.
  • Integrated GL's dynamic semantic models, representing event attributes as state sequences, into VerbNet representations.
  • Utilized VerbNet's semantic roles and cross-class predicates within each subevent representation.

Main Results:

  • Created new, linguistically-grounded semantic representations for VerbNet.
  • The representations explicitly model event participant dynamics and state transitions.
  • Established a framework for applying GL's event structure theories to a large-scale lexical resource.

Conclusions:

  • The developed semantic representations offer a promising approach to enhance NLP inferencing.
  • These representations provide a more nuanced understanding of verb semantics and event structures.
  • Further evaluation is needed to confirm the effectiveness of these representations in practical NLP applications.