Jan Rygl

Automatic Adaptation of Author’s Stylometric Features to Document Types

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).

Building Corpora for Stylometric Research

Authorship recognition, machine translation detection, pedophile identification and other stylometry techniques are daily used in applications for the most widely used languages. On the other hand, under-represented languages lack data sources usable for stylometry research. In this paper, we propose novel algorithm to build corpora containing meta-information required for stylometry experiments (author information, publication time, document heading, document borders) and introduce our tool Authorship Corpora Builder (ACB). We modify data-cleaning techniques for purposes of stylometry field and add a heuristic layer to detect and extract valuable meta-information.

Authorship Attribution: Comparison of Single-Layer and Double-Layer Machine Learning

In the traditional authorship attribution task, forensic linguistic specialists analyse and compare documents to determine who was their (real) author. In the current days, the number of anonymous documents is growing ceaselessly because of Internet expansion. That is why the manual part of the authorship attribution process needs to be replaced with automatic methods. Specialized algorithms (SA) like delta-score and word length statistic were developed to quantify the similarity between documents, but currently prevailing techniques build upon the machine learning (ML) approach.