Finding Related Entities by Retrieving Relations: UIUC at TREC 2009 Entity Track

Abstract

Our goal in participating in the TREC 2009 Entity Track was to study whether relation extraction techniques can help in improving accuracy of the entity finding task. Finding related entities is informational in nature and we wanted to explore if inducing structure on the queries helps satisfy this information need. The research outlook we took was to study techniques that retrieve relations between two entities from a large corpus, and from those, find the most relevant entities that participate in the given relation with another given entity. Instead of aiming at retrieving pages about specific entities, we tried to address the problem of directly finding the entities from the text. Our experimental results show that we were able to find many related entities using relation-based extraction, and ranking entities based on further evidence from the text helps to a certain extent.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517759

Entities

People

  • Chengxiang Zhai
  • Jing He
  • Kavita Ganesan
  • V G Vinod Vydiswaran
  • Yuanhua Lv

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Classification
  • Computer Science
  • Extraction
  • Filters
  • Information Operations
  • Intelligence Collection
  • Language
  • Natural Language Processing
  • Natural Languages
  • Precision
  • Recognition
  • Removal
  • Standards
  • Validation
  • Words (Language)

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.