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        <title>Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention</title>
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    <name type="personal">
        <namePart type="given">Minguang</namePart>
        <namePart type="family">Xiao</namePart>
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    <name type="personal">
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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>Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two annotated nominals, which allows the model to encode each context subsequence independently so as to selectively focus as on the important context information; (2) The hierarchical model consists of two recurrent neural networks (RNNs): the first one learns context representations of the three context subsequences respectively, and the second one computes semantic composition of these three representations and produces a sentence representation for the relationship classification of the two nominals. (3) The attention mechanism is adopted in both RNNs to encourage the model to concentrate on the important information when learning the sentence representations. Experimental results on the SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the state-of-the-art without using any hand-crafted features.</abstract>
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%0 Conference Proceedings
%T Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
%A Xiao, Minguang
%A Liu, Cong
%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 xiao-liu-2016-semantic
%X Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two annotated nominals, which allows the model to encode each context subsequence independently so as to selectively focus as on the important context information; (2) The hierarchical model consists of two recurrent neural networks (RNNs): the first one learns context representations of the three context subsequences respectively, and the second one computes semantic composition of these three representations and produces a sentence representation for the relationship classification of the two nominals. (3) The attention mechanism is adopted in both RNNs to encourage the model to concentrate on the important information when learning the sentence representations. Experimental results on the SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the state-of-the-art without using any hand-crafted features.
%U https://aclanthology.org/C16-1119
%P 1254-1263

Markdown (Informal)

[Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention](https://aclanthology.org/C16-1119) (Xiao & Liu, COLING 2016)

• Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention (Xiao & Liu, COLING 2016)

ACL

• Minguang Xiao and Cong Liu. 2016. Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1254–1263, Osaka, Japan. The COLING 2016 Organizing Committee.