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首页> 外文期刊>Progress in Artificial Intelligence >Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards
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Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

机译：基于深度学习的地质危害的名称实体识别与知识图形构建

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摘要

Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.
机译：构建地质灾害文献的知识图可以促进地质危害文学的重用，并为地质灾害治理提供参考。命名实体识别（NER）作为构建地质灾害知识图表的核心技术，必须面临地质危害文献中指定实体的挑战是在语义中的形式，模糊的形式不同，并且在背景下不确定。这可以在NER分类期间设计在设计实际功能方面的困难。为了解决上述问题，本文提出了一种基于深度学习的NER模型;即，深度多分支大型Bigru-CRF模型，它结合了多分支双向门控复发单元（Bigru）层和条件随机场（CRF）模型。在端到端和监督过程中，所提出的模型自动学习和转换多分支双向GRU层的功能，并通过CRF层增强输出。除了深度多分支的Bigru-CRF模型外，还提出了一种基于模式的语料库施工方法，用于构建深度多分支大型电池CRF模型所需的语料库。实验结果表明，建议深度，多分支大石膏模型表现优于最先进的模型。建议深，多分支Bigru-CRF模型构建了含有34,457个实体节点和84,561个关系的大规模地质灾害文献知识图。

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