Beyond Independent Relevance

By Chengxiang Zhai, William W. Cohen, John D. Lafferty. 2003

150 citations recorded by the source corpus.

We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.

Subfields: Artificial Intelligence, Information Systems

Citations in the corpus

• Relevance-based Word Embedding (2017, neutral): “ o›en share similar contexts. However, this objective is not necessarily equivalent to the main objective of many IR tasks. „e primary objective in many IR methods is to model the notion of relevance [20, 34, 43]. In this paper, we revisit the underlying assumption of typical word embedding methods,”