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IJCAI
2007

Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis

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Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis
Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
Evgeniy Gabrilovich, Shaul Markovitch
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Evgeniy Gabrilovich, Shaul Markovitch
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