Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation
Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation
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DOI：
10.1109/bigcomp.2019.8679233
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发表时间：
2019-04
期刊：
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通讯作者：
Qianwen Wang;M. Iwaihara
中科院分区：
文献类型：
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作者：
Qianwen Wang;M. Iwaihara
当前的实体链接方法通常首先应用命名实体识别（NER）模型来提取命名实体并将其分类到预定义的类别中，然后应用实体消歧模型来将命名实体链接到参考知识库中的对应实体。然而，这些方法忽略了两个任务之间的相互关系。我们的工作联合优化了NER和实体消歧的深度神经模型。在实体消歧任务中，我们的深度神经模型包括递归神经网络和具有注意力机制的卷积神经网络。我们的模型通过同时考虑语义和背景信息（包括维基百科描述页面、提及发生的上下文和实体类型信息）来比较提及和候选实体之间的相似性。实验结果表明，该模型能有效地利用上下文的语义信息，与传统方法相比具有很强的竞争力。
Current entity linking methods typically first apply a named entity recognition (NER) model to extract a named entity and classify it into a predefined category, then apply an entity disambiguation model to link the named entity to a corresponding entity in the reference knowledge base. However, these methods ignore the inter-relations between the two tasks. Our work jointly optimizes deep neural models of both NER and entity disambiguation. In the entity disambiguation task, our deep neural model includes a recursive neural network and convolutional neural network with attention mechanism. Our model compares similarities between a mention and candidate entities by simultaneously considering semantic and background information, including Wikipedia description pages, contexts where the mention occurs, and entity typing information. The experiments show that our model can effectively leverage the semantic information of context, and performs competitively to conventional approaches.