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清华大学学报(自然科学版)  2018, Vol. 58 Issue (3): 260-265    DOI: 10.16511/j.cnki.qhdxxb.2018.26.011
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于λ-主动学习方法的中文微博分词
张婧, 黄德根, 黄锴宇, 刘壮, 孟祥主
大连理工大学 计算机科学与技术学院, 大连 116024
λ-active learning based microblog-oriented Chinese word segmentation
ZHANG Jing, HUANG Degen, HUANG Kaiyu, LIU Zhuang, MENG Xiangzhu
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
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摘要 由于面向中文微博的分词标注语料相对较少,导致基于传统方法和深度学习方法的中文分词系统在微博语料上的表现效果很差。针对此问题,该文提出一种主动学习方法,从大规模未标注语料中挑选更具标注价值的微博分词语料。根据微博语料的特点,在主动学习迭代过程中引入参数λ来控制所选的重复样例的个数,以确保所选样例的多样性;同时,根据样例中字标注结果的不确定性和上下文的多样性,采用Max、Avg和AvgMax这3种策略衡量样例整体的标注价值;此外,用于主动学习的初始分词器除使用当前字的上下文作为特征外,还利用字向量自动计算当前字成为停用字的可能性作为模型的特征。实验结果表明:该方法的F值比基线系统提高了0.84%~1.49%,比目前最优的基于词边界标注(word boundary annotation,WBA)的主动学习方法提升效果更好。
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张婧
黄德根
黄锴宇
刘壮
孟祥主
关键词 文字信息处理中文分词主动学习样例多样性微博语料    
Abstract:Current manual segmented microblog-oriented corpora are inadequate, so both conventional Chinese word segmentation (CWS) systems and deep learning based CWS systems are still not very effective. This paper presents an active learning method that selects samples with high annotation values from unlabelled tweets for microblog-oriented CWS. A parameter is introduced to control the number of repeatedly selected samples that offen occur in microblog data. Three strategies (Max, Avg and AvgMax) are used to evaluate the overall values of each sample. The initial segment character is a stop character which is calculated by taking character embeddings into consideration. Tests demonstrate that this method outperforms the baseline system with F Gains of 0.84%~1.49% and state-of-the-art active learning method word boundary annotation (WBA).
Key wordsword information processing    Chinese word segmentation    active learning    diversity of samples    microblog-oriented data
收稿日期: 2017-08-25      出版日期: 2018-03-14
ZTFLH:  TP391.1  
基金资助:国家自然科学基金资助项目(61672127,61672126)
通讯作者: 黄德根,教授,E-mail:huangdg@dlut.edu.cn     E-mail: huangdg@dlut.edu.cn
作者简介: 张婧(1987-),女,博士研究生。
引用本文:   
张婧, 黄德根, 黄锴宇, 刘壮, 孟祥主. 基于λ-主动学习方法的中文微博分词[J]. 清华大学学报(自然科学版), 2018, 58(3): 260-265.
ZHANG Jing, HUANG Degen, HUANG Kaiyu, LIU Zhuang, MENG Xiangzhu. λ-active learning based microblog-oriented Chinese word segmentation. Journal of Tsinghua University(Science and Technology), 2018, 58(3): 260-265.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.26.011  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I3/260
  图1 算法1
  图2 参数对字上下文差异性的影响
  表1 训练语料和测试语料的统计信息
  表2 初始分词器的分词结果
  图3 参数λ 对主动学习分词结果的影响
  图4 不同选择策略的分词结果
  表3 不同方法的分词结果的最佳F 值
  图5 字的标注结果的不确定性的衡量方法
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