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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 1-17    DOI: 10.11925/infotech.2096-3467.2019.1059
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Review of Chinese Word Segmentation Studies
Tang Lin,Guo Chonghui(),Chen Jingfeng
Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China
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Abstract  

[Objective] This paper summarizes key issues, algorithms, and models from the field of Chinese word segmentation, aiming to provide theoretical basis and practical guidance for future research.[Coverage] We reviewed a total of 109 papers from CNKI, Wanfang Data Knowledge Service Platform, and DBLP Computer Science Bibliography.[Methods] First, we discussed the developments and critical issues facing Chinese word segmentation. Then, we explored algorithms and models for Chinese word segmentation. Finally, we identified popular research topics and trends.[Results] The main challenge facing researchers is creating a Multi-Criteria Learning Model for Chinese Word Segmentation with multiple annotation datasets. The most popular research topic is building Multi-task joint model to finish both Chinese word segmentation and other natural language processing tasks.[Limitations] More research is needed to review studies on unsupervised learning approaches for Chinese word segmentation.[Conclusions] The existing methods of Chinese word segmentation still face challenges in building joint models with multi-perspective, multi-task, and multi-criterion features.

Key wordsChinese      Word      Segmentation      Word      Segmentation      Algorithm      Multi-Criteria      Learning      Joint      Model     
Received: 23 September 2019      Published: 26 April 2020
ZTFLH:  TP393  
Corresponding Authors: Chonghui Guo     E-mail: dlutguo@dlut.edu.cn

Cite this article:

Tang Lin,Guo Chonghui,Chen Jingfeng. Review of Chinese Word Segmentation Studies. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 1-17.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1059     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/1

Distribution of Chinese Word Segmentation
Co-occurrence Network of the Nouns in Chinese Word Segmentation Literature Titles
Topics and Time Distribution of the Conferences Related to Chinese Word Segmentation
年份 作者 研究方法 来源 封闭测试 开放测试
PKU MSR CityU AS PKU MSR CityU AS
2018 Zhang等[12] 结合词典的深度学习方法 AAAI - - - - 96.5 97.8 96.3 95.9
2017 Cai等[13] 基于字和词的深度学习方法 ACL 95.4 97.0 95.4 95.2 95.8 97.1 95.6 95.3
2015 Chen等[14] 基于深度学习的长短期记忆网络 EMNLP 94.3 95.0 - - 96.5 97.4 - -
2012 Sun等[15] 基于丰富特征的现联合学习模型同时学习中文分词和新词发现 ACL 95.4 97.4 94.8 - - - - -
2010 Zhao等[16] 基于字的6位标注方法 TALIP - - - - - 98.3 97.8 96.1
2008 Zhao等[17] 非监督分词辅助基于字的条件随机场方法 SIGHAN 95.4 97.6 96.1 95.7 - - - -
2007 Zhang等[18] 基于词的判别式感知机方法 ACL 94.5 97.2 94.6 96.5 - - - -
2005 Bakeoff 评测结果 评测 95.0 96.4 94.3 95.2 96.9 97.2 96.2 95.6
F-value Test Results on SIGHAN2005 Dataset (%)
Word Cloud of the Key Words
The Distribution of Key Words in Chinese Word Segmentation Literature
Key Words Distribution Related to Neural Networks and Deep Learning
Research Status of Chinese Word Segmentation
Chinese Word Segmentation Flow Chart Based on Deep Learning
年份 作者 来源 研究思路 研究方法 实验使用的数据集
2019 Gong等[24] AAAI 方法改进 模型由多个长短时记忆神经网络(LSTM)和一个切换器组成,可以在这些LSTM之间自动切换。 SIGHAN2005[11](MSR、AS)
SIGHAN2008[84](PKU、CTB、SKIP、CityU、NCC、SXU)
2019 Huang等[85] arXiv 方法改进 基于Bidirectional Encoder Representations (BERT),使用模型剪枝、量化和编译器优化。 CTB6[72]
SIGHAN2005[11] (CityU、PKU、MSR、AS)
SIGHAN2008[84] (SXU)
CoNLL2017[86](UD)
2019 Qiu等[87] arXiv 方法改进 基于Transformer的构架方法采用全连接自注意力机制。 SIGHAN2005[11] (CityU、PKU、MSR、AS)
SIGHAN2008[84] (CTB、SKIP、NCC、SXU)
2019 He等[88] SCI 语料改进 每一个句子的开头和结尾增加人工标记,以区分多粒度语料。再使用LSTM和CRF实现多粒度分词。 SIGHAN2005[11] (MSR、 AS、PKU)
SIGHAN2008[84] (CTB、SKIP、CityU、NCC、SXU)
2019 张文静等[82] 中文信息学报 语料改进
方法改进
模型在网格结构的辅助下,对不同粒度的分词标准都有较强的捕捉能力,且不局限于单一的分词标准。 MSR[89]、PPD[90]、CTB[72]
2017 Chen等[91] ACL 方法改进 借鉴多任务学习的思想,融合多个语料的数据提升共享字向量模块。在此基础上应用对抗网络,把私有信息从共享模块中剥离到各个私有模块中去,既有大数据量的优势,又避免了不同语料之间的相互制约。 SIGHAN2005[11] (MSR、AS)
SIGHAN2008[84](PKU、CTB、SKIP、CityU、NCC、SXU)
2017 Gong等[83] EMNLP 语料改进 构建多粒度语料库。 MSR[89]、PPD[90]、CTB[72]
Comparative Analysis of Multi-granularity and Multi-criterion
任务类型 发表年份 作者 来源 研究方法
自然语言统一处理框架 2008 Collobert等[93] ICML 基于深度学习的CNN模型,首次提出自然语言处理统一框架。该框架同时考虑词性标注、浅层语义分析、命名实体识别、语义角色标注进行多任务学习。
中文分词和词性标注 2004 Ng等[76] EMNLP 定义了一种交叉标记方式,能够同时标注两个任务的结果。
2010 Zhang等[94] ACL 基于线性的单模型,通过柱搜索的方法提升解码效率。
2013 Zeng等[95] ACL 基于半监督的方法,采用基于图标签传播的技术。
2013 Qiu等[96] EMNLP 为异质标注语料构建松散的、具有不确定性的映射,在进行训练同时提高异质标注语料的分词及词性标注的准确性。
2013 Zheng等[97] EMNLP 引入深度学习的方法自动学习特征,从而避免了人工的特征筛选,再结合传统的CRF。
2016 Wang等[98] ICIIP 基于层次长短时记忆,在一个目标函数中同时对多个任务进行联合训练,避免了管道模型性的错误传播问题。
2016 Chen等[99] arXiv 提出一种长距依赖的深度框架,基于联合模型同时完成分词和词性标注任务。
2017 Chen等[100] IJCAI 针对中文分词和词性标注任务提出富特征的深度学习框架,也是一种联合模型。该模型也能够解决长距依赖的问题。
中文分词、词性标注和依存句法 2012 Hatori等[101] ACL 提出一种增量的多任务处理联合模型,首次提出能够同时处理中文分词、词性标注和依存句法的联合模型。
2013 Wang等[102] ACL 使用基于晶格的结构,首先句子被划分为词格,在此基础上进行词性标注和依存句法分析,是一种联合模型。
2016 Guo等[103] IEICE Transactions 提出基于字级别的半监督联合处理模型,能够从部分标注的语料中得到N-Gram特征和依赖子树特征。
2016 Shen等[104] COLING 提出一种新颖的标注方法,该方法能够克服传统基于词法标注的两个问题:不一致性问题和稀疏性问题。
中文分词和依存句法分析 2019 Yan等[105] arXiv 首次提出处理中文分词和依存句法的统一模型,该模型为基于图的深度学习模型。
中文分词和未登录词 2015 Li等[106] TALLIP 提出一种基于字的生成式模型,能同时进行分词和未登录词检测。未登录词主要包括:词典中不包含的词、命名实体和后缀衍生词。
中文分词和非正式词检测 2017 Zhang等[107] IJCAI 中文微博语料中存在非正式用词的问题,传统的分词模型不能很好地对该类语料进行分词。针对这个问题,本文提出基于深度学习的分词和非正式词检测的联合模型。
中文分词和中文正确拼写 2017 Shi等[108] SMP 基于注意力机制的Encoder-Decoder架构提出一种序列到序列的标注方法,能够解决中文分词和中文拼写正确性问题。
中文分词和命名实体识别 2019 Wu等[109] WWW 提出一种新的框架CNER,综合使用了深度学习的CNN、LSTM和CRF。该框架能够在分词的同时识别命名实体。
Analysis of Chinese Word Segmentation Related to Multi-task Joint Model
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