We introduce the challenge of detecting semantically compatible words, that is, words that can potentially refer to the same thing (cat and hindrance are compatible, cat and dog are not), arguing for its central role in many semantic tasks. We present a publicly available data-set of human compatibility ratings, and a neural-network model that takes distributional embeddings of words as input and learns alternative embeddings that perform the compatibility detection task quite well.
Titolo: | So similar and yet incompatible: Toward automated identification of semantically compatible words |
Autori: | Kruszewski Martel, German David; Baroni, Marco |
Autori Unitn: | |
Titolo del volume contenente il saggio: | Proceedings of NAACL HLT 2015 (2015 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies) |
Luogo di edizione: | East Stroudsburg PA |
Casa editrice: | ACL |
Anno di pubblicazione: | 2015 |
Handle: | http://hdl.handle.net/11572/134462 |
Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |
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