Accession Number:

ADA456323

Title:

UMass Robust 2005: Using Mixtures of Relevance Models for Query Expansion

Descriptive Note:

Conference paper

Corporate Author:

MASSACHUSETTS UNIV AMHERST CENTER FOR INTELLIGENT INFORMATION RETRIEVAL

Report Date:

2005-01-01

Pagination or Media Count:

3.0

Abstract:

This paper describes the UMass TREC 2005 Robust Track experiments. For the 2005 Robust Track, we explore whether or not term proximity information and advanced pseudo- relevance feedback methods can be used to achieve good effectiveness on a challenging query set. All experiments used the Indri search engine indexed the full AQUAINT collection of 1,033,461 documents, used a Porter Stemmer and a stopword list of 418 common terms. All runs are automatic. We use Metzlers dependence model formulation to exploit term proximity information, which been shown to significantly improve effectiveness over simple bag of words models. The Indri query language can be used to express dependence model queries. Results indicate that both term proximity and pseudo-relevance are highly effective.

Subject Categories:

  • Information Science
  • Linguistics
  • Operations Research
  • Computer Programming and Software
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE