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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D19-5551.pdf