Computational methods in authorship attribution

Koppel, Moshe; Schler, Jonathan; Argamon, Shlomo

doi:10.1002/asi.20961

Cited by 453 publications

(316 citation statements)

References 112 publications

Supporting

Mentioning

298

Contrasting

Unclassified

Order By: Relevance

“…Koppel et al (Koppel, Schler, & Argamon, 2009) surveyed this line of work, 19 focused on three specific types of problems, and discussed how machine learning methods can be applied to those problems.…”

Section: Discussionmentioning

confidence: 99%

An improved algorithm for unsupervised decomposition of a multi‐author document

Giannella

2015

Asso for Info Science & Tech

View full text Add to dashboard Cite

Abstract AbstractThis paper addresses the problem of unsupervised decomposition of a multi author text document: identifying the sentences that were written by each author assuming the number of authors is unknown. An approach, BayesAD, is developed for solving this problem: apply a Bayesian segmentation algorithm, followed by a segment BayesAD exhibited greater accuracy than AK in all experiments. However, BayesAD has a parameter that needs to be set and which had a non trivial impact on accuracy.Developing an effective method for eliminating this need would be a fruitful direction for future work. When controlling for topic, the accuracy of BayesAD and AK were, in all but one case, worse than a baseline approach wherein one author was assumed to write all sentences in the input text document. Hence, room for improved solutions exists.

show abstract

Section: Discussionmentioning

confidence: 99%

An improved algorithm for unsupervised decomposition of a multi‐author document

Giannella

2015

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Finally, quantitative stylometric analyses have long been used to clarify gross relationships between texts. Standard applications of stylometry include dating literary works and resolving questions of attribution (26)(27)(28)(29)(30). Both ad hoc stylometric analysis and supervised machine learning with stylometric features have proven successful for such applications (31)(32)(33), including for cases in Latin literature (34).…”

Section: Significancementioning

confidence: 99%

“…Computation has long been used for attribution and dating of literary works, problems that are unambiguous in scope and invite binary or numerical answers (27,28). The recent explosion of interest in the digital humanities, however, has led to the key insight that similar computational methods can be repurposed to address questions of literary significance and style, which are often more ambiguous and open-ended.…”

Section: Anomaly Detection Differentiates Suspected Citations From Othermentioning

confidence: 99%

Quantitative criticism of literary relationships

Dexter

Katz

Tripuraneni

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca's main writings are differentiated from the rest of the corpus by subtle but important stylistic features. We offer literary interpretations of the significance of these anomalies, providing quantitative data in support of hypotheses about the use of unusual formal features and the interplay between sound and meaning. The second part of the paper describes a machine-learning approach to the identification and analysis of citational material that Livy loosely appropriated from earlier sources. We extend our approach to map the stylistic topography of Latin prose, identifying the writings of Caesar and his near-contemporary Livy as an inflection point in the development of Latin prose style. In total, our results reflect the integration of computational and humanistic methods to investigate a diverse range of literary questions.authorship attribution | cultural evolution | intertextuality | machine learning | stylometry T he study of literature relies on mapping interactions between texts. Ancient Greek critics understood the tragedies of Aeschylus in part through their relation to Homeric epic, and ancient Roman commentators interpreted words and phrases in texts by citing parallels in other works. Much of literary criticism today rests on understanding these vast networks of intertextuality, which often have profound consequences for the meaning of both individual texts and larger groupings by genre or period (1). Through quantitative analysis of formal elements and their change over time, the study of intertextuality can shed light on the cultural evolution of literature (2).A central challenge in the study of intertextuality is its heterogeneous nature. Literary parallels differ widely in both similarity and scope (Fig. 1A). The relationship between the associated texts can range from obvious (direct quotation) to extremely subtle (artfully constructed indirect references, often referred to as allusions in literary study). Furthermore, parallels can operate on the level of individual words or phrases, short passages, or entire works and can involve verbal, syntactic, phonetic, or metrical features. As illustrated in Fig. 1A, intertexts can be of comparable similarity but very different scope; an adaptation of an entire work, for instance, can be thought of as a collection of many (local) allusions.In this paper, we focus on the quantitative characterization of intertextual rela...

show abstract

“…Various noteworthy literature surveys on authorship attribution have recently been published (Love, 2002;Juola, 2006;Zheng et al, 2006;Koppel et al, 2009;Stamatatos, 2009). In promoting authorship attribution solutions based on statistics, the first methods proposed were based on a unitary invariant value reflecting the particular style of a given author, and varying from one to another (Holmes, 1998).…”

Section: Related Workmentioning

confidence: 99%

Authorship Attribution: A Comparative Study of Three Text Corpora and Three Languages

Savoy

2012

Journal of Quantitative Linguistics

View full text Add to dashboard Cite

The first objective of this paper is carry out three experiments intended to evaluate authorship attribution methods based on three test-collections available in three different languages (English, French, and German). In the first we represent and categorize 52 text excerpts written by nine authors and taken from 19th century English novels. In the second we work with 44 segments from French novels written by eleven authors, mostly from the 19th century. In the third we extract 59 German text excerpts from novels published mainly during the 19th and the beginning of the 20th century, written by 15 authors. The second objective is to analyse performance differences obtained when using word types or lemmas as text representations, and the third objective is to evaluate three authorship attribution schemes, the first of which uses principal component analysis (PCA), the second applies the Delta approach, and the third corresponds to a new authorship attribution method based on specific vocabulary. This concept is computed for a given text (or author profile) and then compared with the entire corpus. Based on this information, we show how a distance measure can be derived and by means of the nearest neighbor approach we suggest a simple and efficient authorship attribution scheme. Based on three test collections and using either word types or lemmas as features, we demonstrate that the suggested classification scheme performs better than the PCA method, and slightly better than the Delta approach.

show abstract

Computational methods in authorship attribution

Cited by 453 publications

References 112 publications

An improved algorithm for unsupervised decomposition of a multi‐author document

An improved algorithm for unsupervised decomposition of a multi‐author document

Quantitative criticism of literary relationships

Authorship Attribution: A Comparative Study of Three Text Corpora and Three Languages

Contact Info

Product

Resources

About