Additive Compositionality of Word Vectors

Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, Alice Oh

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Abstract
Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model’s improved semantic representation performance on word similarity and noisy sentence similarity.
Anthology ID:
D19-5551
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
387–396
Language:
URL:
https://aclanthology.org/D19-5551
DOI:
10.18653/v1/D19-5551
Bibkey:
Cite (ACL):
Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, and Alice Oh. 2019. Additive Compositionality of Word Vectors. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 387–396, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Additive Compositionality of Word Vectors (Seonwoo et al., WNUT 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D19-5551.pdf