A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction
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DOI：
10.18653/v1/n19-1298
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发表时间：
2019-06
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通讯作者：
Duy-Cat Can;Hoang-Quynh Le;Quang-Thuy Ha;Nigel Collier
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文献类型：
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作者：
Duy-Cat Can;Hoang-Quynh Le;Quang-Thuy Ha;Nigel Collier
为了提取句子中两个实体之间的关系，两种常见的方法是(1)使用它们的最短依赖路径（SDP）和(2)使用注意模型来捕获句子的基于上下文的表示。每种方法都有其自身的缺点，即信息缺失或冗余。在这项工作中，我们提出了一个新的模型，结合了这两种方法的优点。这是基于SDP中的基本信息，并通过几种带有内核过滤器的注意机制（即RbSP (richer -但- smarter SDP)）选择的信息进行增强。为了有效地利用RbSP结构背后的表示，我们开发了一个结合词序列的LSTM网络和RbSP的CNN的组合深度神经模型。在SemEval-2010数据集上的实验结果表明，与竞争基准相比，性能有所提高。数据和源代码可从https://github.com/catcd/RbSP获得。
To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but-Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https://github.com/catcd/RbSP.