Authorship identification for heterogeneous documents
Yuta Tsuboi · 2002
The study of authorship identification in Japanese has for the most part been restricted to literary texts using basic statistical methods. In the present study, authors of mailing list messages are identified using a machine learning technique (Support Vector Machines). In addition, the classifier trained on the mailing list data is applied to identify the author of Web documents in order to investigate performance in authorship identification for more heterogeneous documents. Experimental results show better identification performance when we use the features of not only conventional word N-gram information but also of frequent sequential patterns extracted by a data mining technique (PrefixSpan).