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Improving relevance feedback‐based query expansion by the use of a weighted word pairs approach
Author(s) -
Colace Francesco,
De Santo Massimo,
Greco Luca,
Napoletano Paolo
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23331
Subject(s) - computer science , relevance (law) , relevance feedback , information retrieval , query expansion , term (time) , word (group theory) , set (abstract data type) , boolean conjunctive query , representation (politics) , natural language processing , web search query , artificial intelligence , search engine , web query classification , mathematics , image retrieval , physics , geometry , political science , politics , law , image (mathematics) , programming language , quantum mechanics
In this article, the use of a new term extraction method for query expansion (QE) in text retrieval is investigated. The new method expands the initial query with a structured representation made of weighted word pairs ( WWP ) extracted from a set of training documents (relevance feedback). Standard text retrieval systems can handle a WWP structure through custom Boolean weighted models. We experimented with both the explicit and pseudorelevance feedback schemas and compared the proposed term extraction method with others in the literature, such as KLD and RM 3. Evaluations have been conducted on a number of test collections (Text REtrivel Conference [ TREC ]‐6, ‐7, ‐8, ‐9, and ‐10). Results demonstrated that the QE method based on this new structure outperforms the baseline.