@inproceedings{asaadi-rudolph-2017-gradual,
title = "Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis",
author = "Asaadi, Shima and
Rudolph, Sebastian",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2621",
doi = "10.18653/v1/W17-2621",
pages = "178--185",
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.",
}
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%0 Conference Proceedings
%T Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis
%A Asaadi, Shima
%A Rudolph, Sebastian
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F asaadi-rudolph-2017-gradual
%X 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.
%R 10.18653/v1/W17-2621
%U https://aclanthology.org/W17-2621
%U https://doi.org/10.18653/v1/W17-2621
%P 178-185
Markdown (Informal)
[Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis](https://aclanthology.org/W17-2621) (Asaadi & Rudolph, RepL4NLP 2017)
ACL