@inproceedings{liu-etal-2018-task,
title = "Task-oriented Word Embedding for Text Classification",
author = "Liu, Qian and
Huang, Heyan and
Gao, Yang and
Wei, Xiaochi and
Tian, Yuxin and
Liu, Luyang",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1172",
pages = "2023--2032",
abstract = "Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.",
}
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<abstract>Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Task-oriented Word Embedding for Text Classification
%A Liu, Qian
%A Huang, Heyan
%A Gao, Yang
%A Wei, Xiaochi
%A Tian, Yuxin
%A Liu, Luyang
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F liu-etal-2018-task
%X Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.
%U https://aclanthology.org/C18-1172
%P 2023-2032
Markdown (Informal)
[Task-oriented Word Embedding for Text Classification](https://aclanthology.org/C18-1172) (Liu et al., COLING 2018)
ACL
- Qian Liu, Heyan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, and Luyang Liu. 2018. Task-oriented Word Embedding for Text Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2023–2032, Santa Fe, New Mexico, USA. Association for Computational Linguistics.