Authorship verification as a one-class classification problem

Koppel, Moshe; Schler, Jonathan

doi:10.1145/1015330.1015448

Cited by 205 publications

(179 citation statements)

References 19 publications

(9 reference statements)

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“…It should also be noted that it is possible to approach this problem as a verification task (Koppel and Schler 2004) instead of a binary classification one. In this scenario, the methodology is one of novelty or outlier detection where the goal is to decide if a new observation belongs to the training distribution or not.…”

Section: Resultsmentioning

confidence: 99%

Multilingual native language identification

Malmasi¹,

Dras²

2015

Nat. Lang. Eng.

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We present the first comprehensive study of Native Language Identification (NLI) applied to text written in languages other than English, using data from six languages. NLI is the task of predicting an author's first language using only their writings in a second language, with applications in Second Language Acquisition and forensic linguistics. Most research to date has focused on English but there is a need to apply NLI to other languages, not only to gauge its applicability but also to aid in teaching research for other emerging languages. With this goal, we identify six typologically very different sources of non-English second language data and conduct six experiments using a set of commonly used features. Our first two experiments evaluate our features and corpora, showing that the features perform well and at similar rates across languages. The third experiment compares non-native and native control data, showing that they can be discerned with 95 per cent accuracy. Our fourth experiment provides a cross-linguistic assessment of how the degree of syntactic data encoded in partof-speech tags affects their efficiency as classification features, finding that most differences between first language groups lie in the ordering of the most basic word categories. We also tackle two questions that have not previously been addressed for NLI. Other work in NLI has shown that ensembles of classifiers over feature types work well and in our final experiment we use such an oracle classifier to derive an upper limit for classification accuracy with our feature set. We also present an analysis examining feature diversity, aiming to estimate the degree of overlap and complementarity between our chosen features employing an association measure for binary data. Finally, we conclude with a general discussion and outline directions for future work.

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Section: Resultsmentioning

confidence: 99%

Multilingual native language identification

Malmasi¹,

Dras²

2015

Nat. Lang. Eng.

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“…This method provides a robust solution to the authorship verification problem that is independent of language, period and genre and has already been used to settle at least one outstanding literary attribution problem (Koppel and Schler 2004;Koppel et al 2007). …”

Section: Authorship Verificationmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

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452

278

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Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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“…• Author verification (i.e., to decide whether a given text was written by a certain author or not) (Koppel & Schler, 2004). …”

Section: Introductionmentioning

confidence: 99%

A survey of modern authorship attribution methods

Stamatatos

2008

J. Am. Soc. Inf. Sci.

1,060

822

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Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

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Authorship verification as a one-class classification problem

Cited by 205 publications

References 19 publications

Multilingual native language identification

Multilingual native language identification

Computational methods in authorship attribution

A survey of modern authorship attribution methods

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