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        <title>Attention-Based Convolutional Neural Network for Semantic Relation Extraction</title>
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        <namePart type="given">Yatian</namePart>
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    <name type="personal">
        <namePart type="given">Xuanjing</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.</abstract>
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%0 Conference Proceedings
%T Attention-Based Convolutional Neural Network for Semantic Relation Extraction
%A Shen, Yatian
%A Huang, Xuanjing
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F shen-huang-2016-attention
%X Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.
%U https://aclanthology.org/C16-1238
%P 2526-2536

Markdown (Informal)

[Attention-Based Convolutional Neural Network for Semantic Relation Extraction](https://aclanthology.org/C16-1238) (Shen & Huang, COLING 2016)

• Attention-Based Convolutional Neural Network for Semantic Relation Extraction (Shen & Huang, COLING 2016)

ACL

• Yatian Shen and Xuanjing Huang. 2016. Attention-Based Convolutional Neural Network for Semantic Relation Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2526–2536, Osaka, Japan. The COLING 2016 Organizing Committee.