A New Approach for Authorship Attribution
A New Approach for Authorship Attribution
复制标题
作者归属的新方法
DOI：
10.1007/978-981-10-7563-6_1
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Venkannababu
中科院分区：
文献类型：
--
作者：
P. Buddha Reddy;T. Raghunadha Reddy;M. Gopi Chand;A. Venkannababu
作者归属是一种文本分类技术，用于通过分析多个作者的文档来找到未知文档的作者。作者识别的准确性主要取决于作者的写作风格。区分作者写作风格的特征选择是作者归属的重要步骤之一。不同的研究人员提出了一组特征，如字符、单词、句法、语义、结构和可读性特征，以预测未知文档的作者。很少有研究者在作者归属中使用术语重量度量。术语权重度量已被证明是提高文本分类准确率的一种有效方法。现有的作者归属方法使用词袋方法来表示文档向量。在这项工作中，提出了一种新的方法，其中使用文档权重来表示文档向量，而不是使用文档中的特征或术语。在不同分类器的评论语料库上进行了实验，结果表明作者归属比大多数现有方法都要突出。
Authorship attribution is a text classification technique, which is used to find the author of an unknown document by analyzing the documents of multiple authors. The accuracy of author identification mainly depends on the writing styles of the authors. Feature selection for differentiating the writing styles of the authors is one of the most important steps in the authorship attribution. Different researchers proposed a set of features like character, word, syntactic, semantic, structural, and readability features to predict the author of a unknown document. Few researchers used term weight measures in authorship attribution. Term weight measures have proven to be an effective way to improve the accuracy of text classification. The existing approaches in authorship attribution used the bag-of-words approach to represent the document vectors. In this work, a new approach is proposed, wherein the document weight is used to represent the document vector instead of using features or terms in the document. The experimentation is carried out on reviews corpus with various classifiers, and the results achieved for author attribution are prominent than most of the existing approaches.
DOI：
10.1002/asi.v60:3
发表时间：
2009-03
影响因子：
3.5
作者：
N. Shibata;Y. Kajikawa;Y. Takeda;K. Matsushima
通讯作者：
N. Shibata;Y. Kajikawa;Y. Takeda;K. Matsushima