@inproceedings{ren-du-2020-specializing,
title = "Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs",
author = "Ren, Yuanhang and
Du, Ye",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.321/",
doi = "10.18653/v1/2020.coling-main.321",
pages = "3599--3609",
abstract = "Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state-of-the-art retrofitting method such as AR (CITATION). In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute."
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%0 Conference Proceedings
%T Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs
%A Ren, Yuanhang
%A Du, Ye
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ren-du-2020-specializing
%X Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state-of-the-art retrofitting method such as AR (CITATION). In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute.
%R 10.18653/v1/2020.coling-main.321
%U https://aclanthology.org/2020.coling-main.321/
%U https://doi.org/10.18653/v1/2020.coling-main.321
%P 3599-3609
Markdown (Informal)
[Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs](https://aclanthology.org/2020.coling-main.321/) (Ren & Du, COLING 2020)
ACL