Answer Mining from On-Line Documents

Abstract

Mining the answer of a natural language open-domain question in a large collection of on-line documents is made possible by the recognition of the expected answer type in relevant text passages. If the technology of retrieving texts where the answer might be found is well developed, few studies have been devoted to the recognition of the answer type. This paper presents a unified model of answer types for open-domain Question/Answering that enables the discovery of exact answers. The evaluation of the model performed on real-world questions. considers both the correctness and the coverage of the answer types as well as their contribution to answer precision.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2001
Accession Number
ADA460697

Entities

People

  • Marius Pasca
  • Sanda M. Harabagiu

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Computational Linguistics
  • Computer Science
  • Engineering
  • Extraction
  • Feedback
  • Information Retrieval
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Natural Languages
  • Precision
  • Recognition
  • Taxonomy
  • Template Patterns
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development