Automatic Authorship Attribution Research Articles

Evaluation of Feature Extraction Techniques in Automatic Authorship Attribution

Evaluation of Feature Extraction Techniques in Automatic Authorship Attribution

Authorship identification of documents with high content similarity

The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.

Application of the distributed document representation in the authorship attribution task for small corpora

Distributed word representation in a vector space (word embeddings) is a novel technique that allows to represent words in terms of the elements in the neighborhood. Distributed representations can be extended to larger language structures like phrases, sentences, paragraphs and documents. The capability to encode semantic information of texts and the ability to handle high- dimensional datasets are the reasons why this representation is widely used in various natural language processing tasks such as text summarization, sentiment analysis and syntactic parsing. In this paper, we propose to use the distributed representation at the document level to solve the task of the authorship attribution. The proposed method learns distributed vector representations at the document level and then uses the SVM classifier to perform the automatic authorship attribution. We also propose to use the word n-grams (instead of the words) as the input data type for learning the distributed representation model. We conducted experiments over six datasets used in the state-of-the-art works, and for the majority of the datasets, we obtained comparable or better results. Our best results were obtained using the combination of words and n-grams of words as the input data types. Training data are relatively scarce, which did not affect the distributed representation.

Software Library for Authorship Identification

The aim of this paper is to review some methods for text authorship attribution and to discuss the development of a software library with tools for automatic authorship attribution. The presentation is focused on an analysis of two groups of tools oriented to: (1) methods for extraction of features and (2) methods for computing the distance between character strings based on data compression algorithms.

Author discrimination between the Holy Quran and Prophet's statements

Author discrimination consists of checking whether two texts are written by the same author or not. In this investigation, we try to make an author discrimination between the Quran (The holy words and statements of God in the Islamic religion) and the Hadith (statements said by the prophet Muhammad). The Quran is taken in its entirety, whereas for the Prophet’s statements, we chose only the certified texts of the Bukhari book. Thus, three series of experiments are done and commented on. The first series of experiments analyses the two books in a global form (the text of every book is analyzed as a unique big text). It concerns nine different experiments. The second series of experiments analyses the two books in a segmental form (four different segments of text are extracted from every book). It concerns five different experiments. The third series of experiments makes an automatic authorship attribution of the two books in a segmental form by employing several classifiers and several types of features. The sizes of the segments are more or less in the same range (four different text segments, with approximately the same size, are extracted from every book). It concerns two different experiments. This investigation sheds light on an old enigma, which has not been solved for 14 centuries: in fact, all the results of this investigation have shown that the two books should have two different authors.