All-words word sense disambiguation (all-words WSD) is the task of identifying the senses of all words in a document. Since the sense of a word depends on the context, such as the surrounding words, similar words are believed to have similar sets of surrounding words. We therefore predict the target word senses by calculating the distances between the surrounding word vectors of the target words and their synonyms using word embeddings. In addition, we introduce the new idea of concept embeddings, constructed from concept tag sequences created from the results of previous prediction steps. We predict the target word senses using the distances between surrounding word vectors constructed from word and concept embeddings, via a bootstrapped iterative process. Experimental results show that these concept embeddings were able to improve the performance of Japanese all-words WSD.
@InProceedings{SUZUKI18.100, author = {Rui Suzuki and Kanako Komiya and Masayuki Asahara and Minoru Sasaki and Hiroyuki Shinnou}, title = "{All-words Word Sense Disambiguation Using Concept Embeddings}", 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} }
Summary of the paper