Semantic Relatedness Estimation using the Layout Information of Wikipedia Articles

Patrick Chan, Yoshinori Hijikata, Toshiya Kuramochi and Shogo Nishida
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Patrick Chan: Osaka University, Suita, Japan
Yoshinori Hijikata: Osaka University, Suita, Japan
Toshiya Kuramochi: Osaka University, Suita, Japan
Shogo Nishida: Osaka University, Suita, Japan

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2013, vol. 7, issue 2, 30-48

Abstract: Computing the semantic relatedness between two words or phrases is an important problem in fields such as information retrieval and natural language processing. Explicit Semantic Analysis (ESA), a state-of-the-art approach to solve the problem uses word frequency to estimate relevance. Therefore, the relevance of words with low frequency cannot always be well estimated. To improve the relevance estimate of low-frequency words and concepts, the authors apply regression to word frequency, its location in an article, and its text style to calculate the relevance. The relevance value is subsequently used to compute semantic relatedness. Empirical evaluation shows that, for low-frequency words, the authors’ method achieves better estimate of semantic relatedness over ESA. Furthermore, when all words of the dataset are considered, the combination of the authors’ proposed method and the conventional approach outperforms the conventional approach alone.

Date: 2013
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