The HLTCOE Approach to the TREC 2012 KBA Track

Abstract

Our team submitted runs for the TREC KBA Cumulative Citation Recommendation task. This task involves labeling over 300 million documents for whether they are relevant and/or central to particular entities already in a database. For this task, we used an SVM classifier that uses unigrams and named entities as binary features. In this paper, we describe our work for the 2012 evaluation and the results we obtained.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2012
Accession Number
ADA580904

Entities

People

  • Brian Kjersten
  • Paul Mcnamee

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Applied Computer Science
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Computational Processes
  • Computer Science
  • Computing-Related Activities
  • Data Science
  • Databases
  • Machine Learning
  • Numbers
  • Precision
  • Standards
  • Supervised Machine Learning
  • Test And Evaluation
  • Training
  • Word Lists

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Information Retrieval
  • Neural Network Machine Learning.