@inproceedings{ren-etal-2018-neural,
title = "Neural Relation Classification with Text Descriptions",
author = "Ren, Feiliang and
Zhou, Di and
Liu, Zhihui and
Li, Yongcheng and
Zhao, Rongsheng and
Liu, Yongkang and
Liang, Xiaobo",
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/C18-1100",
pages = "1167--1177",
abstract = "Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities{'} text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the {``}intra-sentence{''} aspect and the {``}cross-sentence{''} aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.",
}
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<abstract>Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the “intra-sentence” aspect and the “cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.</abstract>
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%0 Conference Proceedings
%T Neural Relation Classification with Text Descriptions
%A Ren, Feiliang
%A Zhou, Di
%A Liu, Zhihui
%A Li, Yongcheng
%A Zhao, Rongsheng
%A Liu, Yongkang
%A Liang, Xiaobo
%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 ren-etal-2018-neural
%X Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities’ text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the “intra-sentence” aspect and the “cross-sentence” aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.
%U https://aclanthology.org/C18-1100
%P 1167-1177
Markdown (Informal)
[Neural Relation Classification with Text Descriptions](https://aclanthology.org/C18-1100) (Ren et al., COLING 2018)
ACL
- Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, and Xiaobo Liang. 2018. Neural Relation Classification with Text Descriptions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1167–1177, Santa Fe, New Mexico, USA. Association for Computational Linguistics.