UDEL/SMU at TREC 2009 Entity Track

Abstract

We report our methods and experiment results from the collaborative participation of the InfoLab group from University of Delaware and the school of Information Systems from Singapore Management University in the TREC 2009 Entity track. Our general goal is to study how we may apply language modeling approaches and natural language processing techniques to the task. Specifically, we proposed to find supporting information based on segment retrieval, to extract entities using Stanford NER tagger, and to rank entities based on a previously proposed probabilistic framework for expert finding.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517766

Entities

People

  • Hui Fang
  • Jing Jiang
  • Swapna Gottipati
  • Wei Xing Zheng

Organizations

  • University of Delaware

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence Software
  • Demographic Cohorts
  • Extraction
  • Generative Models
  • Information Operations
  • Information Systems
  • Language
  • Machine Learning
  • Models
  • Named Entity Recognition
  • Natural Language Processing
  • Natural Languages
  • Probability
  • Standards
  • Universities

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computational Modeling and Simulation
  • Research Science/Academic Research

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval