Dear colleagues,

We would like to announce a new research paper that uses Wikipedia for computing semantic relatedness of natural language texts.

Evgeniy Gabrilovich and Shaul Markovitch (2007). ''Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis''. Proceedings of The 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007 http://www.cs.technion.ac.il/~gabr/papers/ijcai-2007-sim.pdf

ABSTRACT

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.

Kind regards,

Evgeniy.

-- Evgeniy Gabrilovich Ph.D. student in Computer Science Department of Computer Science, Technion - Israel Institute of Technology Technion City, Haifa 32000, Israel Email: gabr@cs.technion.ac.il WWW: http://www.cs.technion.ac.il/~gabr