Explicit semantic analysis

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In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectorial representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf–idf matrix of the text corpus and a document (string of words) is represented as the centroid of the vectors representing its words. Typically, the text corpus is Wikipedia, though other corpora including the Open Directory Project have been used.[1]

ESA was designed by Evgeniy Gabrilovich and Shaul Markovitch as a means of improving text categorization[2] and has been used by this pair of researchers to compute what they refer to as "semantic relatedness" by means of cosine similarity between the aforementioned vectors, collectively interpreted as a space of "concepts explicitly defined and described by humans", where Wikipedia articles (or ODP entries, or otherwise titles of documents in the knowledge base corpus) are equated with concepts. The name "explicit semantic analysis" contrasts with latent semantic analysis (LSA), because the use of a knowledge base makes it possible to assign human-readable labels to the concepts that make up the vector space.[3][1]

ESA, as originally posited by Gabrilovich and Markovitch, operates under the assumption that the knowledge base contains topically orthogonal concepts. However, it was later shown by Anderka and Stein that ESA also improves the performance of information retrieval systems when it is based not on Wikipedia, but on the Reuters corpus of newswire articles, which does not satisfy the orthogonality property; in their experiments, Anderka and Stein used newswire stories as "concepts".[4] To explain this observation, links have been shown between ESA and the generalized vector space model.[5] Gabrilovich and Markovitch replied to Anderka and Stein by pointing out that their experimental result was achieved using "a single application of ESA (text similarity)" and "just a single, extremely small and homogenous test collection of 50 news documents".

Cross-language explicit semantic analysis (CL-ESA) is a multilingual generalization of ESA.[6] CL-ESA exploits a document-aligned multilingual reference collection (e.g., again, Wikipedia) to represent a document as a language-independent concept vector. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations.

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  1. ^ a b Ofer Egozi, Shaul Markovitch and Evgeniy Gabrilovich (2011). "Concept-Based Information Retrieval using Explicit Semantic Analysis". ACM Transactions on Information Systems 29 (2). 
  2. ^ Evgeniy Gabrilovich and Shaul Markovitch. Overcoming the brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge. Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), pp. 1301-1306, 2006.
  3. ^ Evgeniy Gabrilovich and Shaul Markovitch. Computing semantic relatedness using Wikipedia-based Explicit Semantic Analysis. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1606-1611, 2007.
  4. ^ Maik Anderka and Benno Stein. The ESA retrieval model revisited. Proceedings of the 32nd International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 670-671, 2009.
  5. ^ Thomas Gottron, Maik Anderka and Benno Stein. Insights into explicit semantic analysis. Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pp. 1961-1964, 2011.
  6. ^ Martin Potthast, Benno Stein, and Maik Anderka. A Wikipedia-based multilingual retrieval model. Proceedings of the 30th European Conference on IR Research (ECIR), pp. 522-530, 2008.