@inproceedings{torabi-asr-etal-2018-querying,
title = "Querying Word Embeddings for Similarity and Relatedness",
author = "Torabi Asr, Fatemeh and
Zinkov, Robert and
Jones, Michael",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1062",
doi = "10.18653/v1/N18-1062",
pages = "675--684",
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.",
}
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%0 Conference Proceedings
%T Querying Word Embeddings for Similarity and Relatedness
%A Torabi Asr, Fatemeh
%A Zinkov, Robert
%A Jones, Michael
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F torabi-asr-etal-2018-querying
%X 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.
%R 10.18653/v1/N18-1062
%U https://aclanthology.org/N18-1062
%U https://doi.org/10.18653/v1/N18-1062
%P 675-684
Markdown (Informal)
[Querying Word Embeddings for Similarity and Relatedness](https://aclanthology.org/N18-1062) (Torabi Asr et al., NAACL 2018)
ACL
- Fatemeh Torabi Asr, Robert Zinkov, and Michael Jones. 2018. Querying Word Embeddings for Similarity and Relatedness. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 675–684, New Orleans, Louisiana. Association for Computational Linguistics.