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Entity perception of Two-Step-Matching framework for public opinions

Journal of Safety Science and Resilience 2020年第1期1卷 36-43页

作者： Ren-De Li Hao-Tian Ma Zi-Yi Wang Qiang Guo Jian-Guo Liu Library and Research Center of Computer Systems Science University of Shanghai for Science and TechnologyShanghai 200093PR China School of Accountancy and Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and EconomicsShanghai 200433PR China School of Humanities Shanghai University of Finance and EconomicsShanghai 200433PR China Institute of Sina WRD Big Data Shanghai 201204PR China

Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public *** this paper,a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities ***,potential entities are extracted by bilstm-crf model and characteristic words by TF-IDF model from public ***,the first matching is implemented between potential entities and an official business directory by Jaro-Winkler distance ***,in order to find the pre-cise one,an industry-characteristic dictionary is developed into the second matching *** precise entity is identified according to the count of characteristic words matching to industry-characteristic *** addition,associated rate(global indicator)and accuracy rate(sample indicator)are defined for evaluation of matching *** results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95,averagely enhanced by 32%and 30%above the case of using the first matching process *** framework provides the method to find the true target entity of really wanted expression from public opinions.

关键词： Entity perception bilstm-crf model Jaro-Winkler distance algorithm User comments Public opinions

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Chinese Named Entity Recognition for Hazard And Operability Analysis Text

Chinese Named Entity Recognition for Hazard And Operability ...

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第32届中国控制与决策会议

作者： FangGuo Li BeiKe Zhang Dong Gao College of Information Science and Technology Beijing University of Chemical Technology

To solve the problem that it is difficult to identify the key Chinese entity information in the hazard and operability analysis text, a deep neural network model based on bidirectional long short-term memory and conditional random field（bilstm-crf） is proposed to identify key named entities in the text. In the word vector pre-training process, bidirectional encoder representation from transformers（BERT） model is used to pre-train word vectors instead of the static word vectors in the traditional word2 vec model, and then obtain context-related dynamic word vectors, to improve the representational ability of word vectors, and solve the problem of word boundary division when word vectors are used in Chinese corpus training. This model has the ability of complete the task of Chinese named entity recognition. The F1 value on the test corpus reaches 93.31%, which is 21.82% higher than conditional random field（crf） Baseline, and 4.49% higher than the traditional bilstm-crf model. The experimental results show that the BERT-bilstm-crf model is effective for the named entity recognition（NER） task of the hazard and operability analysis text, and it is helpful to automatically extract the relationship between the entities in the hazard and operability analysis text and build safety analysis knowledge graph.

关键词： Chinese named entity recognition Hazard and operability analysis BERT bilstm-crf model

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