Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model

doi:10.1016/j.aei.2021.101416

CQUET-IR

	Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model
	Li, Ren 1; Mo, Tianjin 1,2; Yang, Jianxi 1; Li, Dong 1; Jiang, Shixin 1; Wang, Di 1
	2021-10
发表期刊	ADVANCED ENGINEERING INFORMATICS
ISSN	1474-0346
EISSN	1873-5320
卷号	50
摘要	As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level fea-tures encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model out-performs other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.
关键词	Bridge inspection Named entity recognition Machine reading comprehension BERT Information retrieval Knowledge representation Natural language processing systems Automatic information extraction Context representation Contextual feature Question answering systems Reading comprehension Sequence features
DOI	10.1016/j.aei.2021.101416
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary
WOS记录号	WOS:000701801400001
出版者	ELSEVIER SCI LTD
EI入藏号	20213810906156
EI分类号	723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 903.3 Information Retrieval and Use
原始文献类型	Article ; Journal article (JA)
出版地	OXFORD
引用统计	被引频次：42[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3533
专题	重庆电子科技职业大学
作者单位	1.Chongqing Jiaotong Univ, Sch Informat Sci & Engn, Chongqing 400074, Peoples R China; 2.Chongqing Coll Elect Engn, Chongqing 401331, Peoples R China
推荐引用方式 GB/T 7714	Li, Ren,Mo, Tianjin,Yang, Jianxi,et al. Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model[J]. ADVANCED ENGINEERING INFORMATICS,2021,50.
APA	Li, Ren,Mo, Tianjin,Yang, Jianxi,Li, Dong,Jiang, Shixin,&Wang, Di.(2021).Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model.ADVANCED ENGINEERING INFORMATICS,50.
MLA	Li, Ren,et al."Bridge inspection named entity recognition via BERT and lexicon augmented machine reading comprehension neural model".ADVANCED ENGINEERING INFORMATICS 50(2021).