Congress 1172
  • David E. Losada, Javier Parapar, Álvaro Barreiro
  • 5th Spanish Conference in Information Retrieval. Zaragoza, España. 2018

Cost-effective construction of Information Retrieval test collections

In this paper we describe our recent research on effective construction of Information Retrieval collections. Relevance assessments are a core component of test collections, but they are expensive to produce. For each test query, only a sample of documents in the corpus can be assessed for relevance. We discuss here a class of document adjudication methods that iteratively choose documents based on reinforcement learning. Given a pool of candidate documents supplied by multiple retrieval systems, the production of relevance assessments is modeled as a multi-armed bandit problem. These bandit-based algorithms identify relevant documents with minimal effort. One instance of these models has been adopted by NIST to build the test collection of the TREC 2017 common core track.
Keywords: Information Retrieval evaluation, relevance assessments, pooling, multi-armed bandits
Canonical link