Publication details
Automatic Adaptation of Author's Stylometric Features to Document Types
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | Text, Speech, and Dialogue - 17th International Conference |
| MU Faculty or unit | |
| Citation | |
| web | http://www.tsdconference.org/tsd2014/download/preprints/575.pdf |
| Doi | http://dx.doi.org/10.1007/978-3-319-10816-2_7 |
| Field | Informatics |
| Keywords | authorship verification; feature selection; machine learning; stylome; stylometric features |
| Description | Many Internet users face the problem of anonymous documents and texts with a counterfeit authorship. The number of questionable documents exceeds the capacity of human experts, therefore a universal automated authorship identification system supporting all types of documents is needed. In this paper, five predominant document types are analysed in the context of the authorship verification: books, blogs, discussions, comments and tweets. A method of an automatic selection of authors’ stylometric features using a double-layer machine learning is proposed and evaluated. Experiments are conducted on ten disjunct train and test sets and a method of an efficient training of large number of machine learning models is introduced (163,700 models were trained). |
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