The Smart/Empire Tipster IR System

Abstract

The primary goal of the Cornell/Sabir TIPSTER Phase III project is to develop techniques to improve the end-user efficiency of information retrieval (IR) systems. We have focused our investigations in four related research areas: 1. High Precision Information Retrieval. The goal of our research in this area is to increase the accuracy of the set of documents given to the user. 2. Near-Duplicate Detection. The goal of our work in near-duplicate detection is to develop methods for delineating or removing from the set of retrieved documents any information that the user has already seen. 3. Context-Dependent Document Summarization. The goal of our research in this area is to provide for each document a short summary that includes only those portions of the document relevant to the query. 4. Context-Dependent Multi-Document Summarization. The goal of our research in this area is to provide a short summary for an entire group of related documents that includes only query-related portions.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA631829

Entities

People

  • Chris Buckley
  • Claire Cardie
  • David Pierce
  • Janet Walz
  • Kiri Wagstaff
  • Mandar Mitra
  • Scott Mardis

Organizations

  • Cornell University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Automated Text Summarization
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Detection
  • Grammars
  • Graphical User Interface
  • Identification
  • Information Retrieval
  • Language
  • Lisp Programming Language
  • Natural Language Processing
  • Natural Languages
  • Object Recognition
  • Recognition
  • United States

Fields of Study

  • Computer science

Readers

  • Business Analytics
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval