@inproceedings{liang-etal-2017-combining,
title = "Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text",
author = "Liang, Dongyun and
Xu, Weiran and
Zhao, Yinge",
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/W17-2606",
doi = "10.18653/v1/W17-2606",
pages = "43--47",
abstract = "Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important short-distance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on two dataset of relation classification, SemEval-2010 Task8 and a large-scale one we compile from informal text, show that our model achieves a competitive result in the former and state-of-the-art with the other.",
}
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%0 Conference Proceedings
%T Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text
%A Liang, Dongyun
%A Xu, Weiran
%A Zhao, Yinge
%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 liang-etal-2017-combining
%X Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important short-distance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on two dataset of relation classification, SemEval-2010 Task8 and a large-scale one we compile from informal text, show that our model achieves a competitive result in the former and state-of-the-art with the other.
%R 10.18653/v1/W17-2606
%U https://aclanthology.org/W17-2606
%U https://doi.org/10.18653/v1/W17-2606
%P 43-47
Markdown (Informal)
[Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text](https://aclanthology.org/W17-2606) (Liang et al., RepL4NLP 2017)
ACL