Conference Paper

Shallow Text Analysis and Machine Learning for Authorship Attribtion.

Conference: Computational Linguistics in the Netherlands 2004, Selected Papers from the Fifteenth CLIN Meeting, December 17, Leiden Centre for Linguistics
Source: DBLP

ABSTRACT Current advances in shallow parsing and machine learning allow us to use results from these fields in a methodology for Authorship Attribution. We report on experimen ts with a corpus that consists of newspaper articles about national current affairs by different journalists from the Belgian newspaper De Standaard. Because the documents are in a similar genre, register, and range of topics, token-based (e.g., sentence length) and lexical features (e.g., vocabulary richness) can be kept roughly constant over the different authors. This allows us to focus on the use of syntax-based features as possible predictors for an author 's style, as well as on those token-based features that are predictive to author style more than to topic or register. These style characteristics are not under the author's conscious control and therefore good clues for Authorship Attribution. Machine Learning methods (TiMBL and the WEKA software package) are used to select informative combinations of syntactic, token-based a nd lexical features and to predict authorship of unseen documents. The combination of these features can be considered an implicit profile that characterizes the style of an author.

Download full-text


Available from: Kim Luyckx, Sep 27, 2015
9 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Style is an integral part of natural language in written, spoken or machine generated forms. Humans have been dealing with style in language since the beginnings of language itself, but computers and machine processes have only recently begun to process natural language styles. Automatic processing of styles poses two interrelated challenges: classification and transformation. There have been recent advances in corpus classification, automatic clustering and authorship attribution along many dimensions but little work directly related to writing styles directly and even less in transformation. In this paper we examine relevant literature to define and operationalize a notion of "style" which we employ to designate style markers usable in classification machines. A measurable reading of these markers also helps guide style transformation algorithms. We demonstrate the concept by showing a detectable stylistic shift in a sample piece of text relative to a target corpus. We present ongoing work in building a comprehensive style recognition and transformation system and discuss our results.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The aim of this paper is to explore text topic influence in authorship attribution. Specifically, we test the widely accepted belief that stylometric variables commonly used in authorship attribution are topic-neutral and can be used in multi-topic corpora. In order to investigate this hypothesis, we created a special corpus, which was controlled for topic and author simultaneously. The corpus consists of 200 Modern Greek newswire articles written by two authors in two different topics. Many commonly used stylometric variables were calculated and for each one we performed a two-way ANOVA test, in order to estimate the main effects of author, topic and the interaction between them. The results showed that most of the variables exhibit considerable correlation with the text topic and their exploitation in authorship analysis should be done with caution.
    Proceedings of the SIGIR 2007 International Workshop on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, PAN 2007, Amsterdam, Netherlands, July 27, 2007; 01/2007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a new corpus for computational stylometry, more specifically authorship attribution and the prediction of author personality from text. Because of the large number of authors (145), the corpus will allow previously impossible studies of variation in features considered predictive for writing style. The innovative meta-information (personality profiles of the authors) associated with these texts allows the study of personality prediction, a not yet very well researched aspect of style. In this paper, we describe the contents of the corpus and show its use in both authorship attribution and personality prediction. We focus on features that have been proven useful in the field of author recognition. Syntactic features like part-of-speech n-grams are generally accepted as not being under the author's conscious control and therefore providing good clues for predicting gender or authorship. We want to test whether these features are helpful for personality prediction and authorship attribution on a large set of authors. Both tasks are approached as text categorization tasks. First a document representation is constructed based on feature selection from the linguistically analyzed corpus (using the Memory-Based Shallow Parser (MBSP)). These are associated with each of the 145 authors or each of the four components of the Myers-Briggs Type Indicator (Introverted-Extraverted, Sensing-iNtuitive, Thinking-Feeling, Judging- Perceiving). Authorship attribution on 145 authors achieves results around 50% accuracy. Preliminary results indicate that the first two personality dimensions can be predicted fairly accurately.
    01/2008; , ISBN: 2-9517408-4-0
Show more