作者识别研究综述

张洋; 江铭虎

doi:10.16383/j.aas.c200654

作者识别研究综述

doi: 10.16383/j.aas.c200654

张洋^1,,
江铭虎^1,

1.
清华大学人文学院计算语言学实验室北京 100084

基金项目: 国家自然科学基金(62036001)资助

详细信息

作者简介:
张洋：清华大学人文学院中文系博士研究生. 主要研究方向为作者识别, 文本分类, 情感分析. E-mail: yumaoqiuq@163.com

江铭虎：清华大学人文学院中文系教授. 主要研究方向为自然语言处理, 脑与语言认知, 模式识别, 人工智能. 本文通信作者. E-mail: jiang.mh@mail.tsinghua.edu.cn

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出版历程
- 收稿日期: 2020-08-14
- 录用日期: 2021-02-09
- 网络出版日期: 2021-03-17
- 刊出日期: 2021-11-18

A Review on Authorship Identification Research

ZHANG Yang^1
,,
JIANG Ming-Hu^1
,

1.
Lab of Computational Linguistics, School of Humanities, Tsinghua University, Beijing 100084

Funds: Supported by National Natural Science Foundation of China (62036001)

More Information

Author Bio:
ZHANG Yang　Ph. D. candidate in the Department of Chinese Language and Literature, School of Humanities, Tsinghua University. His research interest covers authorship identification, text categorization, sentiment analysis

JIANG Ming-Hu　Professor in the Department of Chinese Language and Literature, School of Humanities, Tsinghua University. His research interest covers natural language processing, brain and language cognition, pattern recognition, artificial intelligence. Corresponding author of this paper

摘要

摘要: 作者识别是根据已知文本推断未知文本作者的交叉学科. 其传统研究通常基于文学或语言学的经验知识, 而现代研究则主要依靠数学方法量化作者的写作风格. 近些年, 随着认知科学、系统科学和信息技术的发展, 作者识别受到越来越多研究者的关注. 本文主要站在计算语言学的角度综述作者识别领域现代研究中的方法和思路. 首先, 简要介绍了作者识别的发展历程. 然后, 详述了文体风格特征、作者识别方法以及该领域中多层面的研究. 接着介绍了与作者识别相关的一些评测、数据集及评价指标. 最后, 指出该领域存在的一些问题, 结合这些问题分析并展望了作者识别的发展趋势.
- 作者识别 /
- 文体学 /
- 写作风格 /
- 评价指标
Abstract: Authorship identification is an interdisciplinary subject of inferring the author of unknown texts based on the known texts. The traditional research of authorship identification is generally based on the empirical knowledge of literature or linguistics, while the modern research mostly relies on mathematical methods to quantify the author＇ s writing style. In recent years, with the development of cognitive science, system science and information technology, more and more researchers pay attention to authorship identification. This paper mainly reviews the methods and ideas in modern research in the field of authorship identification from the perspective of computational linguistics. First, the development history of authorship identification is introduced briefly. Then, the stylometry, authorship identification methods and multi-faceted research in this realm are expounded. Next, some evaluations, data sets and evaluation metrics related to authorship identification are explicated. Finally, some problems in this domain are pointed out, while the development trend of authorship identification is analyzed and forecasted combined with these problems.
- Authorship identification /
- stylometry /
- writing style /
- evaluation metrics
注释:

1) ¹ https://umlt.infotech.monash.edu/?page_id=266² http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm³ https://umlt.infotech.monash.edu/?page_id=152⁴ https://www.cs.cmu.edu/~./enron/⁵ https://drive.google.com/drive/folders/1hlIWVSt0dfy8fz8d4w RzZItl-LCo5BH1?usp=sharing

2) ² http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

3) ³ https://umlt.infotech.monash.edu/?page_id=152

4) ⁴ https://www.cs.cmu.edu/~./enron/

5) ⁵ https://drive.google.com/drive/folders/1hlIWVSt0dfy8fz8d4w RzZItl-LCo5BH1?usp=sharing

6) ⁶ https://archive.ics.uci.edu/ml/datasets/Reuter_50_50⁷ https://pan.webis.de

7) ⁷ https://pan.webis.de

HTML全文

图 1 作者识别流程图

Fig. 1 Flow diagram of authorship identification

下载: 全尺寸图片幻灯片

表 1 文体风格特征对比表

Table 1 Comparative table of stylometry

文体特征	特征细分	获取难易度	应用广泛度	其他
字符特征	字符数量, 字符 n-gram, 字符错误	非常容易, 可直接提取	很高	主题独立, 可捕捉书写错误, 特征维度容易过大, 导致数据稀疏
词汇特征	词长, 词频, 词汇丰富度, 单词 n-gram, 词拼写错误	容易, 直接提取或分词后提取	很高	主题相关, 可捕捉书写错误
句法特征	短语或句子结构, 词性 n-gram, 句法 n-gram, 重写规则频率	较难, 深层句法特征需借助句法解析器	低	主题独立, 通常不具有连续性, 解析器容易引入噪声
语义特征	同义词, 语义依赖	困难, 需借助语义分析工具	很低	主题相关, 通常作为其他特征的补充, 很少独立使用

下载: 导出CSV

表 2 无监督方法对比表

Table 2 Comparative table of unsupervised method

方法	模型	策略	算法
k 均值聚类	k 中心聚类	样本与类中心距离最小	迭代算法
层次聚类	聚类树	类内样本距离最小	启发式算法
高斯混合聚类	高斯混合模型	似然函数最大	期望最大化算法
LSA	矩阵分解模型	平方损失最小	奇异值分解
LDA	LDA 模型	后验概率估计	吉布斯抽样, 变分推理

下载: 导出CSV

表 3 有监督方法对比表

Table 3 Comparative table of supervised method

方法	模型类型	模型特点	学习策略	稳定性	准确率
NB	生成模型	特征与类别的联合概率分布, 条件独立假设	极大似然估计, 最大后验概率估计	高	低
SVM	判别模型	分离超平面, 核技巧	极小化正则化合页损失, 软间隔最大化	中	高
DT	判别模型	分类树、回归树	正则化的极大似然估计	中	中
KNN	判别模型	特征空间, 样本点	无	低	中
NN	判别模型	神经元拓扑结构	目标函数最小化	中	偏高

下载: 导出CSV

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