Publisher = "Global Wordnet Association",Ībstract = "Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD). Cite (Informal): Detecting Most Frequent Sense using Word Embeddings and BabelNet (Arora et al., GWC 2016) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Detecting Most Frequent Sense using Word Embeddings and et",īooktitle = "Proceedings of the 8th Global WordNet Conference (GWC)", In Proceedings of the 8th Global WordNet Conference (GWC), pages 21–25, Bucharest, Romania. Detecting Most Frequent Sense using Word Embeddings and BabelNet. Anthology ID: 2016.gwc-1.4 Volume: Proceedings of the 8th Global WordNet Conference (GWC) Month: 27-30 January Year: 2016 Address: Bucharest, Romania Venue: GWC SIG: Publisher: Global Wordnet Association Note: Pages: 21–25 Language: URL: DOI: Bibkey: arora-etal-2016-detecting Cite (ACL): Harpreet Singh Arora, Sudha Bhingardive, and Pushpak Bhattacharyya. However, this approach can be applied to any language provided that word embeddings are available for that language. The MFS is detected for six languages viz., English, Spanish, Russian, German, French and Italian. We compare word embedding of a word with its sense embeddings to obtain the MFS with the highest similarity. are used for generating sense embeddings. The semantic features from BabelNet viz., synsets, gloss, relations, etc. In this paper, we present our work on Most Frequent Sense (MFS) detection using Word Embeddings and BabelNet features. Beating the first sense heuristics is a challenging task for these systems. Over the years, various supervised, unsupervised and knowledge based WSD systems have been proposed. Abstract Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |