Concept-Based Information Retrieval Using Explicit Semantic Analysis

TLDR

Keyword‑based IR often yields inaccurate results because different terms can describe the same concept, requiring semantic knowledge that traditional methods lack. The authors aim to develop a concept‑based retrieval system that augments keyword representations with automatically extracted Wikipedia concepts using Explicit Semantic Analysis. They implement ESA to generate concept features from Wikipedia and employ self‑generated labeled data for feature selection, enabling the system to focus retrieval. Evaluated on multiple TREC datasets, the system outperforms prior state‑of‑the‑art methods, demonstrating the importance of high‑quality feature selection.

Abstract

Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.