Abstract
We introduce a vector space representation of concepts using Wikipedia graph structure to calculate semantic relatedness. The proposed method starts from the neighborhood graph of a concept as the primary form and transfers this graph into a vector space to obtain the final representation. The proposed method achieves state of the art results on various relatedness datasets.
Combining the vector space representation with standard coherence model, we show that the proposed relatedness method performs successfully in Word Sense Disambiguation (WSD). We then suggest a different formulation for coherence to demonstrate that, in a short enough sentence, there is one key entity that can help disambiguate every other entity. Using this finding, we provide a vector space based method that can outperform the standard coherence model in a significantly shorter computation time.
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Notes
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We use Wikipedia 20160305 dump for relatedness.
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The dataset is publicly available on the project website.
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Acknowledgments
This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Boeing Company, and Mitacs. We would also like to thank Jeannette C.M. Janssen for comments that greatly improved the manuscript.
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Sajadi, A., Milios, E.E., Keselj, V. (2017). Vector Space Representation of Concepts Using Wikipedia Graph Structure. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_48
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