Personae: a Corpus for Author and Personality Prediction from Text
Personae: a Corpus for Author and Personality Prediction from Text
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Personae：从文本进行作者和性格预测的语料库
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Walter Daelemans
中科院分区：
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作者：
Kim Luyckx;Walter Daelemans
我们提出了一个新的语料库计算文体学，更具体地说，作者归属和作者个性的预测文本。由于大量的作者（145），语料库将允许以前不可能的研究被认为是预测写作风格的特征的变化。与这些文本相关的创新元信息（作者的个性简介）允许研究个性预测，这是一个尚未得到很好研究的风格方面。在本文中，我们描述了语料库的内容，并显示其在作者归属和人格预测。我们专注于已被证明在作者识别领域有用的功能。句法特征如词性n元语法一般被认为不受作者的意识控制，因此为预测性别或作者提供了很好的线索。我们想测试这些特征是否有助于对大量作者进行人格预测和作者归属。这两个任务都是文本分类任务。首先，从语言分析语料库（使用基于记忆的浅层解析器（MBSP））的特征选择的基础上构建的文档表示。这些都与145位作者中的每一位或迈尔斯-布里格斯类型指标的四个组成部分（内向-外向，感觉-直觉，思考-感觉，判断-感知）中的每一个相关联。对145位作者的作者归属达到了约50%的准确率。初步结果表明，前两个人格维度可以相当准确地预测。
We present a new corpus for computational stylometry, more specifically authorship attribution and the prediction of author personality from text. Because of the large number of authors (145), the corpus will allow previously impossible studies of variation in features considered predictive for writing style. The innovative meta-information (personality profiles of the authors) associated with these texts allows the study of personality prediction, a not yet very well researched aspect of style. In this paper, we describe the contents of the corpus and show its use in both authorship attribution and personality prediction. We focus on features that have been proven useful in the field of author recognition. Syntactic features like part-of-speech n-grams are generally accepted as not being under the authors conscious control and therefore providing good clues for predicting gender or authorship. We want to test whether these features are helpful for personality prediction and authorship attribution on a large set of authors. Both tasks are approached as text categorization tasks. First a document representation is constructed based on feature selection from the linguistically analyzed corpus (using the Memory-Based Shallow Parser (MBSP)). These are associated with each of the 145 authors or each of the four components of the Myers-Briggs Type Indicator (Introverted-Extraverted, Sensing-iNtuitive, Thinking-Feeling, Judging-Perceiving). Authorship attribution on 145 authors achieves results around 50%-accuracy. Preliminary results indicate that the first two personality dimensions can be predicted fairly accurately.