Concept-based information retrieval using explicit semantic analysis
|Concept-based information retrieval using explicit semantic analysis|
|Author(s)||Egozi O., Markovitch S., Gabrilovich E.|
|Published in||ACM Transactions on Information Systems|
|Keyword(s)||Concept-based retrieval, Explicit semantic analysis, Feature selection, Semantic search (Extra: Concept-based, Concept-based retrieval, Data sets, Explicit semantics, Feature selection methods, High quality, Human knowledge, Keyword-based retrieval, Labeled data, Labeled training data, Semantic search, Term co-occurrence, Text feature, Text representation, Wikipedia, World knowledge, Feature extraction, Information retrieval systems, Knowledge representation, Search engines, Semantics, Information retrieval)|
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Concept-based information retrieval using explicit semantic analysis is a 2011 journal article written in English by Egozi O., Markovitch S., Gabrilovich E. and published in ACM Transactions on Information Systems.
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 keywordbased 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.
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