LASSO: A Tool for Surfing the Answer Net Metadata

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Title

  • Main Title LASSO: A Tool for Surfing the Answer Net

Creator

  • Author: Moldovan, Dan I.
    Creator Type: Personal
    Creator Info: Southern Methodist University
  • Author: Harabagiu, Sanda M.
    Creator Type: Personal
    Creator Info: Southern Methodist University
  • Author: Paşca, Marius. 1974-
    Creator Type: Personal
    Creator Info: Southern Methodist University
  • Author: Mihalcea, Rada, 1974-
    Creator Type: Personal
    Creator Info: University of North Texas; Southern Methodist University
  • Author: Goodrum, Richard A.
    Creator Type: Personal
    Creator Info: Southern Methodist University
  • Author: Gîrju, Corina R.
    Creator Type: Personal
    Creator Info: Southern Methodist University
  • Author: Rus, Vasile
    Creator Type: Personal
    Creator Info: Southern Methodist University

Contributor

  • Organizer of meeting: National Institute of Standards and Technology (U.S.)
    Contributor Type: Organization

Publisher

  • Name: National Institute of Standards and Technology (U.S.)
    Place of Publication: [Gaithersburg, Maryland]

Date

  • Creation: 1999-11

Language

  • English

Description

  • Content Description: This paper discusses LASSO, a tool for surfing the answer net.
  • Physical Description: 9 p.

Subject

  • Keyword: LASSO
  • Keyword: natural language processing
  • Keyword: information retrieval
  • Keyword: paragraph indexing

Source

  • Conference: Eighth Text Retrieval Conference (TREC), 1999, Gaithersburg, Maryland, United States

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Engineering
    Code: UNTCOE

Rights

  • Rights Access: public

Resource Type

  • Paper

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc83331

Degree

  • Academic Department: Computer Science and Engineering

Note

  • Display Note: Abstract: This paper presents the architecture, operation and results obtained with the LASSO system developed in the Natural Language Processing Laboratory at SMU. The system relies on a combination of syntactic and semantic techniques, and lightweight abductive inference to find answers. The search for the answer is based on a novel form of indexing called paragraph indexing. A score of 55.5% for short answers and 64.5% for long answers was achieved.