结合gazetteers和句法依存树的中文命名实体识别

doi:10.3778/j.issn.1002-8331.2102-0130

计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (18): 227-232.DOI: 10.3778/j.issn.1002-8331.2102-0130

结合gazetteers和句法依存树的中文命名实体识别

方红，苏铭，冯一铂，张澜

1.上海第二工业大学文理学部，上海 201209
2.上海第二工业大学工学部，上海 201209
3.喀什大学数学与统计学院，新疆喀什 844000

出版日期:2022-09-15 发布日期:2022-09-15

Chinese Named Entity Recognition Combined with Gazetteers and Syntactic Dependency Tree

FANG Hong, SU Ming, FENG Yibo, ZHANG Lan

1.College of Arts and Sciences, Shanghai Polytechnic University, Shanghai 201209, China
2.College of Engineering, Shanghai Polytechnic University, Shanghai 201209, China
3.College of Mathematics and Statistics, Kashgar University, Kashgar, Xinjiang 844000, China

Online:2022-09-15 Published:2022-09-15

摘要/Abstract

摘要： 中文命名实体识别在机器翻译、智能问答等下游任务中起着重要作用。提出一种新的基于gazetteers和句法依存树的中文命名实体识别方法，旨在解决由于字符向量缺少词信息和词之间的句法依赖结构信息而导致的错误传递问题。该方法将句子中的gazetteers信息和句法依存树信息形成图，再通过自适应门控图神经网络（adapted gated graph neural networks，AGGNN）将其融入到字符向量中，从而使得每个字向量很好地获取词汇间的语义关系，提升识别准确率。通过在Ecommerce、Resume、QI等数据集的验证，新的方法可以使得中文实体识别的准确率得到较大提升。

关键词: gazetteers, 句法依存树, 序列标注, 自适应门控图神经网络（AGGNN）, 双向长短记忆网络（BiLSTM）, 条件随机场（CRF）

Abstract: Chinese named entity recognition plays an important role in downstream tasks such as machine translation and intelligent question answering. A new Chinese named entity recognition algorithm based on gazetteers and syntactic dependency tree is proposed in this paper. To solve the problem of error transmission caused by the lack of word information in character vector and syntactic dependent structure information between words. This algorithm forms a graph of the gazetteers information and the syntactic dependency tree information in the sentence and then integrates it into the character vector through adaptive gated graph neural networks（AGGNN）, so that the semantic relationship between words is obtained well in each character vector and the recognition accuracy is improved. Through the verification in Ecommerce, Resume, QI and other data sets, the new method can greatly improve the accuracy of Chinese entity recognition.

Key words: gazetteers, syntactic dependency tree, sequence labeling, adaptive gated graph neural networks（AGGNN）, bi-directional long short-term memory（BiLSTM）, conditional random field（CRF）

方红, 苏铭, 冯一铂, 张澜. 结合gazetteers和句法依存树的中文命名实体识别[J]. 计算机工程与应用, 2022, 58(18): 227-232.

FANG Hong, SU Ming, FENG Yibo, ZHANG Lan. Chinese Named Entity Recognition Combined with Gazetteers and Syntactic Dependency Tree[J]. Computer Engineering and Applications, 2022, 58(18): 227-232.

参考文献

[1] HE J，WANG H.Chinese named entity recognition and word segmentation based on character[C]//Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing，2008.
[2] LI H，HAGIWARA M，LI Q，et al.Comparison of the impact of word segmentation on name tagging for Chinese and Japanese[C]//Language Resources and Evaluation Conference，2014：2532-2536.
[3] 刘小安，彭涛.基于卷积神经网络的中文景点识别研究[J].计算机工程与应用，2020，56（4）：145-150.
LIU X A，PENG T.Research on Chinese scenic spot named entity recognition based on convolutional neural network[J].Computer Engineering and Applications，2020，56（4）：145-150.
[4] 谢腾，杨俊安，刘辉.基于BERT-BiLSTM-CRF模型的中文实体识别[J].计算机系统应用，2020，27（7）：48-55.
XIE T，YANG J A，LIU H.Chinese entity recognition based on BERT-BiLSTM-CRF model[J].Computer Systems & Applications，2020，29（7）：48-55.
[5] ZHANG Y，YANG J.Chinese NER using lattice LSTM[J].arXiv：1805.02023，2018.
[6] LIU J，WU F，WU C，et al.Neural Chinese word segmentation with dictionary[J].Neurocomputing，2019，338：46-54.
[7] ZHANG Q，LIU X，FU J.Neural networks incorporating dictionaries for Chinese word segmentation[C]//AAAI，2018 Symposium，2018：5682-5689.
[8] DING R，XIE P，ZHANG X，et al.A neural multi-digraph model for Chinese NER with gazetteers[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics，2019：1462-1467.
[9] 霍振朗.基于深度学习的命名实体识别研究[D].广州：华南理工大学，2018.
HUO Z L.Research on named entity recognition with deep learning[D].Guangzhou：South China University of Technology，2018.
[10] DI CARO L，GRELLA M.Sentiment analysis via dependency parsing[J].Computer Standards & Interfaces，2013，35（5）：442-453.
[11] HONNIBAL M，JOHNSON M.An improved non-monotonic transition system for dependency parsing[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，2015：1373-1378.
[12] CETOLI A，BRAGAGLIA S，O’HARNEY A D，et al.Graph convolutional networks for named entity recognition[J].arXiv：1709.10053，2017.
[13] LI Y，TARLOW D，BROCKSCHMIDT M，et al.Gated graph sequence neural networks[J].arXiv：1511.05493，2015.
[14] CHEN X，QIU X，ZHU C，et al.Long short-term memory neural networks for chinese word segmentation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，2015：1197-1206.
[15] CHUNG J，GULCEHRE C，CHO K，et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv：1412.3555，2014.
[16] LIN B Y，XU F F，LUO Z，et al.Multi-channel BilSTM-CRF model for emerging named entity recognition in social media[C]//Proceedings of the 3rd Workshop on Noisy User-generated Text，2017：160-165.
[17] MARCHEGGIANI D，TITOV I.Encoding sentences with graph convolutional networks for semantic role labeling[J].arXiv：1703.04826，2017.
[18] YANG J，ZHANG Y.Ncrf++：an open-source neural sequence labeling toolkit[J].arXiv：1806.05626，2018.
[19] WANG Q，ZHOU Y，RUAN T，et al.Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition[J].Journal of Biomedical Informatics，2019，92：103133.

结合gazetteers和句法依存树的中文命名实体识别

Chinese Named Entity Recognition Combined with Gazetteers and Syntactic Dependency Tree

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

[1]	杨春霞, 瞿涛, 吴佳君. 基于BiGCN和IAM的方面级情感分类模型[J]. 计算机工程与应用, 2022, 58(11): 178-186.
[2]	李文亮，杨秋翔，秦权. 多特征混合模型文本情感分析方法[J]. 计算机工程与应用, 2021, 57(19): 205-213.
[3]	田梓函，李欣. 基于BERT-CRF模型的中文事件检测方法研究[J]. 计算机工程与应用, 2021, 57(11): 135-139.
[4]	汪瀛寰，薛婵，包先雨，吴共庆. 触发词与属性值对联合抽取方法研究[J]. 计算机工程与应用, 2020, 56(9): 168-174.
[5]	李博，康晓东，张华丽，王亚鸽，陈亚媛，白放. 采用Transformer-CRF的中文电子病历命名实体识别[J]. 计算机工程与应用, 2020, 56(5): 153-159.
[6]	刘小安，彭涛. 基于卷积神经网络的中文景点识别研究[J]. 计算机工程与应用, 2020, 56(4): 140-145.
[7]	孙宝山，李玮. 窥视孔连接的循环网络在中文分词上的研究[J]. 计算机工程与应用, 2019, 55(19): 160-165.
[8]	杜玉龙，李建增，张岩，范聪. 基于深度交叉CNN和免交互GrabCut的显著性检测[J]. 计算机工程与应用, 2017, 53(3): 32-40.
[9]	谷晶晶，周国栋. 基于分词与词性标注的汉语逗号自动分类[J]. 计算机工程与应用, 2015, 51(18): 120-125.