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首页> 外文期刊>Information Sciences: An International Journal >BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction
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BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction

机译：Bilu-nemh：Bilu神经编码的提及提取的超图

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The natural language processing (NLP) denotes a technique used to process data such as text and speech. Some of the fundamental research in NLP includes the named entity recognition, which recognizes the named entities (i.e., persons and companies) from texts, the semantic parsing, which converts a natural language utterance to a logical form, and the co-reference resolution, which extracts the nouns (including pronouns and noun phrases) pointing to the same reference body. In this paper, we focus on the mention extraction and classification, proposing a neural-encoded mention-hypergraph model named the BILU-NEMH to extract the mention entities from a content. The proposed BILU-NEMH model combines a mention hypergraph model with the encoding schema and neural network. The proposed model can effectively capture the overlapping mention entities of an unbounded length. The proposed model was verified by the experiments, and the obtained experimental results showed that the proposed model achieved better performance and greater effectiveness than the existing related models on most standard datasets. (C) 2019 Elsevier Inc. All rights reserved.
机译：自然语言处理（NLP）表示用于处理文本和语音等数据的技术。 NLP中的一些基本研究包括命名实体识别，它识别来自文本的名称实体（即人员和公司），语义解析，将自然语言话语转换为逻辑形式，以及共同参考分辨率，其中提取指向相同的参考主体的名词（包括代词和名词短语）。在本文中，我们专注于提及提取和分类，提出名为Bilu-NEMH的神经编码的提及超图模型，以从内容中提取提及实体。建议的Bilu-NEMH模型将提及的超图模型与编码模式和神经网络相结合。所提出的模型可以有效地捕获无界长度的重叠的实体。通过实验验证了所提出的模型，所获得的实验结果表明，该模型比大多数标准数据集上的现有相关模型实现了更好的性能和更大的效率。 （c）2019 Elsevier Inc.保留所有权利。

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