Abstract
Learning word representations to capture the semantics and compositionality of language has received much research interest in natural language processing. Beyond the popular vector space models, matrix representations for words have been proposed, since then, matrix multiplication can serve as natural composition operation. In this work, we investigate the problem of learning matrix representations of words. We present a learning approach for compositional matrix-space models for the task of sentiment analysis. We show that our approach, which learns the matrices gradually in two steps, outperforms other approaches and a gradient-descent baseline in terms of quality and computational cost.- Anthology ID:
- W17-2621
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 178–185
- Language:
- URL:
- https://aclanthology.org/W17-2621
- DOI:
- 10.18653/v1/W17-2621
- Cite (ACL):
- Shima Asaadi and Sebastian Rudolph. 2017. Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 178–185, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis (Asaadi & Rudolph, RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W17-2621.pdf