Querying Word Embeddings for Similarity and Relatedness

Fatemeh Torabi Asr, Robert Zinkov, Michael Jones


Abstract
Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data. Skipgram generates a set of word and context embeddings, the latter typically discarded after training. We demonstrate the usefulness of context embeddings in predicting asymmetric association between words from a recently published dataset of production norms (Jouravlev & McRae, 2016). Our findings suggest that humans respond with words closer to the cue within the context embedding space (rather than the word embedding space), when asked to generate thematically related words.
Anthology ID:
N18-1062
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
675–684
Language:
URL:
https://www.aclweb.org/anthology/N18-1062
DOI:
10.18653/v1/N18-1062
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1062.pdf