Authorship Attribution

In this paper we present an approach to automatic authorship attribution dealing with real-world (or unrestricted) text. Our method is based on the computational analysis of the input text using …...

We present a simple method for language inde- pendent and task independent text categoriza- tion learning, based on character-level n-gram language models. Our approach uses simple information theoretic principles and …...

We present a method for computer- assisted authorship attribution based on character-level n-gram language mod- els. Our approach is based on sim- ple information theoretic principles, and achieves improved performance …...

. Linguistic information can help improve evaluation of simi- larity between documents; however, the kind of linguistic information to be used depends on the task. In this paper, we show …...

We present an investigation of recently proposed character and word sequence kernels for the task of authorship attribu- tion based on relatively short texts. Per- formance is compared with two …...

Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extracted from …...

AbstractSubjectivity analysis and authorship attribution are very popular areas of research. However, work in these two areas has been done separately. We believe that by combining information about subjectivity in …...

The problem of authorship attribution – at- tributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest …...

This paper proposes the use of local his- tograms (LH) over character n-grams for au- thorship attribution (AA). LHs are enriched histogram representations that preserve se- quential information in documents; …...

Much of the writing styles recognized in rhetorical and composition theories involve deep syntactic elements. However, most previous research for computational sty- lometric analysis has relied on shallow lexico-syntactic patterns. …...

The emergence of social media (networks, blogs, web forums) has given people numer- ous opportunities to share their personal sto- ries, including details of their health. Although users mostly post …...

Work on authorship attribution has tradition- ally focused on long texts. In this work, we tackle the question of whether the author of a very short text can be successfully …...

AbstractWe define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, de-lexicalized parse of source texts generated by a standard lexicalized parser, which we apply …...

Authorship detection is a challenging task due to many design choices the user has to decide on. The performance highly depends on the right set of features, the amount of …...

Character n-grams have been identified as the most successful feature in both single- domain and cross-domain Authorship Attribu- tion (AA), but the reasons for their discrimina- tive value were not …...

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes exten- sions to structural correspondence learning needed to make it appropriate for the …...

AbstractAuthorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way …...

AbstractMost existing research on authorship attribution uses various lexical, syntactic and semantic features. In this paper we demonstrate an effective template-based approach for combining various syntactic features of a document …...

Delta measures are a well-established and popular family of authorship attribution methods, especially for literary texts. N-gram tracing is a novel method for authorship attribution designed for very short texts, …...

In this paper, we analyze relationships between word pairs and evaluate their idiosyncratic properties in the applied context of authorship attribution. Specifically, on three literary corpora we optimize word pair …...

AbstractApproaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely …...

AbstractAuthorship attribution typically uses all information representing both content and style whereas attribution based only on stylistic aspects may be robust in cross-domain settings. This paper analyzes different linguistic aspects …...

AbstractAuthorship attribution is an active research area which has been prevalent for many decades. Nevertheless, the majority of approaches consider problem sizes of a few candidate authors only, making them …...

AbstractIn this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction model when using personalized versus generic word …...

AbstractTraditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of …...

AbstractRecent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even …...

AbstractThe success of authorship attribution relies on the presence of linguistic features specific to individual authors. There is, however, limited research assessing to what extent authorial style remains constant when …...

AbstractGiven the potential misuse of recent advances in synthetic text generation by language models (LMs), it is important to have the capacity to attribute authorship of synthetic text. While stylometric …...

AbstractAuthorship attribution infers the likely author of an unsigned, single-authored document from a pool of candidates. Despite recent advances, a lack of standard, reproducible testbeds for Chinese language documents impedes …...

AbstractAuthorship attribution is the task of identifying the author of a given text. The key is finding representations that can differentiate between authors. Existing approaches typically use manually designed features …...

AbstractIn this paper we propose a study of a relatively novel problem in authorship attribution research: that of classifying the stylome of characters in a literary work. We choose as …...