An Unsupervised Approach to Recognizing Discourse Relations

Abstract

We present an unsupervised approach to recognizing discourse relations of CONTRAST, EXPLANATION-EVIDENCE, CONDITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA462240

Entities

People

  • Abdessamad Echihabi
  • Daniel Marcu

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Additives (Chemicals)
  • Computational Linguistics
  • Contrast
  • Information Operations
  • Information Science
  • Language
  • Linguistics
  • Low Noise
  • Machine Learning
  • Natural Language Processing
  • Natural Languages
  • Standards
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Theoretical Analysis.