Improving the effectiveness of information retrieval with local context analysis

TLDR

Automatic query expansion techniques, categorized as global or local, aim to address word mismatch, but existing local methods, though generally more effective, lack robustness and can degrade retrieval when few top‑ranked documents are relevant. The study proposes local context analysis, a technique that selects expansion terms based on cooccurrence with query terms in the top‑ranked documents. Local context analysis selects expansion terms by analyzing cooccurrence with query terms in the top‑ranked documents. Experiments on multiple English and non‑English collections demonstrate that local context analysis yields more effective and consistent retrieval results.

Abstract

Techniques for automatic query expansion have been extensively studied in information research as a means of addressing the word mismatch between queries and documents. These techniques can be categorized as either global or local. While global techniques rely on analysis of a whole collection to discover word relationships, local techniques emphasize analysis of the top-ranked documents retrieved for a query. While local techniques have shown to be more effective that global techniques in general, existing local techniques are not robust and can seriously hurt retrieved when few of the retrieval documents are relevant. We propose a new technique, called local context analysis, which selects expansion terms based on cooccurrence with the query terms within the top-ranked documents. Experiments on a number of collections, both English and non-English, show that local context analysis offers more effective and consistent retrieval results.