[1]张柯文,李 翔,严云洋,等.基于多特征双向门控神经网络的领域专家实体抽取方法[J].南京师大学报(自然科学版),2021,(01):128-135.[doi:10.3969/j.issn.1001-4616.2021.01.018]
 Zhang Kewen,Li Xiang,Yan Yunyang,et al.Domain Expert Entity Extraction Method Based on Multi-FeatureBidirectional Gated Neural Network[J].Journal of Nanjing Normal University(Natural Science Edition),2021,(01):128-135.[doi:10.3969/j.issn.1001-4616.2021.01.018]
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基于多特征双向门控神经网络的领域专家实体抽取方法()
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《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

卷:
期数:
2021年01期
页码:
128-135
栏目:
·计算机科学与技术·
出版日期:
2021-03-15

文章信息/Info

Title:
Domain Expert Entity Extraction Method Based on Multi-FeatureBidirectional Gated Neural Network
文章编号:
1001-4616(2021)01-0128-08
作者:
张柯文李 翔严云洋朱全银马甲林
淮阴工学院计算机与软件工程学院,江苏 淮安 223005
Author(s):
Zhang KewenLi XiangYan YunyangZhu QuanyinMa Jialin
Faculty of Computer and Software Engineering,Huaiyin Institute of Technology,Huai’an 223005,China
关键词:
命名实体识别自然语言处理信息提取多特征边界特征
Keywords:
named entity recognitionnatural language processinginformation extractionmulti-featureboundary feature
分类号:
TP301.6
DOI:
10.3969/j.issn.1001-4616.2021.01.018
文献标志码:
A
摘要:
命名实体识别是自然语言处理和信息提取的基本任务,传统专家命名实体识别方法存在过度依赖人工特征标注和分词效果、专家简介中大量专业新词无法识别等问题. 本文提出一种基于多特征双向门控神经网络结构并结合条件随机场模型进行领域专家实体抽取方法. 该方法首先通过构建领域专家语料库以训练实体抽取模型; 接着,使用Bert方法进行字嵌入表示,对语料库专业领域词汇构造要素进行特征分析并提取边界特征; 然后,利用双向门控神经网络和注意力机制有效获取特定词语长距离依赖关系; 最后,结合条件随机场模型实现命名实体识别. 在同一数据集上进行5种方法实验比较分析,结果表明该模型较BiLSTM-CRF和IDCNN-CRF方法F1值提高9.98%以上.
Abstract:
Named entity recognition is the basic task of natural language processing(NLP)and information extraction(IE). Traditional expert named entity recognition methods have problems,such as excessive reliance on artificial feature labeling and word segmentation effects,and the inability to recognize a large number of professional new words in the expert profile. This paper proposes a method based on multi-features bidirectional gated neural network structure combined with conditional random field model for the domain expert entity extraction. Firstly,train the entity extraction model by constructing a domain expert corpus. Secondly,use the Bert method to represent the word embedding,and perform feature analysis on the vocabulary structure elements of the professional field of the corpus and extract the boundary features. Thirdly,use the bidirectional gated neural network and attention mechanism to effectively obtain the long-distance dependence of specific words. Finally,combine the conditional random field model to achieve named entity recognition. The experimental comparison and analysis of five methods on the same data set show that the F1 value of the model is improved by more than 9.98% compared with BiLSTM-CRF and IDCNN-CRF.

参考文献/References:

[1] 邹博伟,钱忠,陈站成,等. 面向自然语言文本的否定性与不确定性信息抽取[J]. 软件学报,2016,27(2):309-328.
[2]LI J,SUN A,HAN J,et al. A survey on deep learning for named entity recognition[J]. IEEE transactions on knowledge and data engineering,2020,32(3):1558-2191.
[3]GE H,CAVERLEE J,LU H. Taper:a contextual tensor-based approach for personalized expert recommendation[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston,2016:261-268.
[4]LI X,WANG Z,GAO S,et al. An intelligent context-aware management framework for cold chain logistics distribution[J]. IEEE transactions on intelligent transportation systems,2019,20(12):4553-4566.
[5]BARTOLI A,DE LORENZO A,MEDVET E,et al. Active learning of regular expressions for entity extraction[J]. IEEE transactions on cybernetics,2017,48(3):1067-1080.
[6]ZHANG Y,YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne,2018:1554-1564.
[7]汪诚愚,何晓丰,宫学庆,等. 面向上下位关系预测的词嵌入投影模型[J]. 计算机学报,2019,43(5):868-883.
[8]MORWAL S,JAHAN N,CHOPRA D. Named entity recognition using hidden Markov model(HMM)[J]. International journal on natural language computing,2012,1(4):15-23.
[9]MCCALLUM A,FREITAG D,PEREIRA F C N. Maximum entropy Markov models for information extraction and segmentation[C]//Proceedings of International Conference on Machine Learning. Stanford,2000:591-598.
[10]LAFFERTY J,MCCALLUM A,PEREIRA F C N. Conditional random fields:probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning. Williams College,MA,2001:282-289.
[11]DEVLIN J,CHANG M W,LEE K,et al. Bert:pre-training of deep bidirectional transformers for language understanding[J]. Computation and language,2018,23(2):3-19.
[12]COLLOBERT R,WESTON J,BOTTOU L,et al. Natural language processing(almost)from scratch[J]. Journal of machine learning research,2011,12(1):2493-2537.
[13]STRUBELL E,VERGA P,BELANGER D,et al. Fast and accurate entity recognition with iterated dilated convolutions[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen,2017:2670-2680.
[14]HUANG Z,XU W,YU K. Bidirectional LSTM-CRF models for sequence tagging[J]. Computer science,2015(8):1508-1518.
[15]WANG S,LI Y,LIU N,et al. Noisy-data-disposing algorithm of data clean on the attribute level[J]. Computer engineering,2005(9):86-87.



[16]张华平,吴林芳,张芯铭,等. 领域知识图谱小样本构建与应用[J]. 人工智能,2020(1):113-124.

[17]唐明,朱磊,邹显春. 基于Word2Vec的一种文档向量表示[J]. 计算机科学,2016,43(6):214-217,269.
[18]MIKOLOV T,SUTSKEVER I,CHEN K,et al. Distributed representations of words and phrases and their compositionality[J]. Advances in neural information processing systems,2013,26:3111-3119.
[19]PENNINGTON J,SOCHER R,MANNING C D. Glove:global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha,2014:1532-1543.
[20]严云洋,瞿学新,朱全银,等. 基于离群点检测的分类结果置信度的度量方法[J]. 南京大学学报(自然科学版),2019,55(1):102-109.
[21]GOUTTE C,GAUSSIER E. A probabilistic interpretation of precision,recall and F-score,with implication for uation[C]//Proceedings of European Conference on Information Retri. Springer,Berlin,Heidelberg,2005:345-359.

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备注/Memo

备注/Memo:
收稿日期:2020-08-08.
基金项目:国家自然科学基金项目(71874067、61602202)、国家重点研发计划项目(2018YFB1004904)、江苏省产学研合作项目(BY2020067、BY2020309)、江苏省农业科技自主创新资金项目(CX203074)、淮阴工学院研究生科技创新计划项目(HGYK202024).
通讯作者:李翔,博士,副教授,研究方向:机器学习、数据挖掘、推荐系统. E-mail:hyitlixiang@hotmail.com
更新日期/Last Update: 2021-03-15