Wikipedia is a very popular source of encyclopedic knowledge which provides highly reliable articles in a variety of domains. This richness and popularity created a strong motivation among NLP researchers to develop relatedness measures between Wikipedia concepts. In this paper, we introduce WORD (Wikipedia Oriented Relatedness Dataset), a new type of concept relatedness dataset, composed of 19,276 pairs of Wikipedia concepts. This is the first human annotated dataset of Wikipedia concepts, whose purpose is twofold. On the one hand, it can serve as a benchmark for evaluating concept-relatedness methods. On the other hand, it can be used as supervised data for developing new models for concept relatedness prediction. Among the advantages of this dataset compared to its term-relatedness counterparts, are its built-in disambiguation solution, and its richness with meaningful multiword terms. Based on this benchmark we develop a new tool, named WORT (Wikipedia Oriented Relatedness Tool), for measuring the level of relatedness between pairs of concepts. We show that the relatedness predictions ofWORT outperform state of the art methods.
@InProceedings{EIN DOR18.445, author = {Liat Ein Dor and Alon Halfon and Yoav Kantor and Ran Levy and Yosi Mass and Ruty Rinott and Eyal Shnarch and Noam Slonim}, title = "{Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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