Million Query Track 2007 Overview

Abstract

The Million Query (1MQ) track ran for the first time in TREC 2007. It was designed to serve two purposes. First, it was an exploration of ad-hoc retrieval on a large collection of documents. Second, it investigated questions of system evaluation, particularly whether it is better to evaluate using many shallow judgments or fewer thorough judgments. Participants in this track were assigned two tasks: (1) run 10,000 queries against a 426Gb collection of documents at least once and (2) judge documents for relevance with respect to some number of queries. Section 1 describes how the corpus and queries were selected, details the submission formats, and provides a brief description of all submitted runs. Section 2 provides an overview of the judging process, including a sketch of how it alternated between two methods for selecting the small set of documents to be judged. Sections 3 and 4 provide details of those two selection methods, developed at UMass and NEU, respectively. The sections also provide some analysis of the results. In Section 6 we present some statistics about the judging process, such as the total number of queries judged, how many by each approach, and so on. We present some additional results and analysis of the overall track in Sections 7 and 8.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA477493

Entities

People

  • Ben Carterette
  • Blagovest Dachev
  • Evangelos Kanoulas
  • James Allan
  • Javed A. Aslam
  • Virgil Pavlu

Organizations

  • University of Massachusetts Amherst

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computer Science
  • Data Science
  • Estimators
  • Information Retrieval
  • Information Science
  • Language
  • Network Science
  • Precision
  • Probability
  • Random Variables
  • Sampling
  • Standards
  • Statistical Samples
  • Statistical Sampling
  • Statistics

Readers

  • Business Analytics
  • Computational Linguistics
  • Instructional Design and Training Evaluation.