Determining an author's native language by mining a text for errors

In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning ? Analogies, Concept learning, Connectionism and neural nets, Induction, Knowledge acquisition, Language acquisition, Parameter learning General Terms Algorithms, Measurement, Experimentation Keywords Text mining, author profiling

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Moshe Koppel, Jonathan Schler, Kfir Zigdon
• Keywords: Author Native Language, Data Mining, KDD 2005, Stylistic Text Features, Various Stylistic Idiosyncrasies