Linguistic profiling for author recognition and verification

Halteren, H. van

doi:10.3115/1218955.1218981

Cited by 86 publications

(63 citation statements)

References 5 publications

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“…A number of studies used the output of syntactic text chunkers and parsers to create features, and found that they could considerably improve results based on traditional word based analysis alone (Baayen et al 1996;Stamatatos et al 2000Stamatatos et al , 2001Gamon 2004;van Halteren 2004;Chaski 2005;Uzuner and Katz 2005;Hirst & Feiguina 2007). Many studies have used the frequencies of short sequences of parts-of-speech (or combinations of parts-of-speech and other classes of words) as a simple method for approximating syntactic features for this purpose (Argamon-Engelson et al 1998;Kukushkina et al 2001;De Vel et al 2001;Koppel et al 2002;Koppel & Schler 2003;Chaski 2005;Koppel et al 2005Koppel et al , 2006avan Halteren et al 2005;Zhao et al 2006;Zheng et al 2006).…”

Section: Syntax and Parts-of-speechmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

461

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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Section: Syntax and Parts-of-speechmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

461

278

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“…A variety of performance measures have been used in previous work on this task including false acceptance and false rejection rates [60,17], accuracy [25,26], recall, precision, F 1 [30], balanced error rate [19], recall-precision graphs [26] macro-average precision and recall [1], and ROC graphs [22]. Unfortunately, these measures are not able to explicitly estimate the ability of an approach to leave problems unanswered-a fact which is crucial in a cost-sensitive task like this.…”

Section: Related Workmentioning

confidence: 99%

“…Previous work on author verification has been evaluated using sample texts in one language only (Greek [60], Dutch [17,30], English [25,26]) and a specific genre (newspaper articles [60], student essays [30], fiction [25], newswire stories [19], poems [19], blogs [26]). Author verification was also included in previous editions of PAN: the author identification task at PAN-2011 included three author verification problems [1], PAN-2013 focused on author verification and provided corpora in English, Greek, and Spanish [22].…”

Section: Related Workmentioning

confidence: 99%

Improving the Reproducibility of PAN’s Shared Tasks:

Potthast

Gollub

Rangel

et al. 2014

Lecture Notes in Computer Science

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Potthast, M.; Gollub, T.; Rangel, F.; Rosso, P.; Stamatatos, E.; Abstract This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling.To improve the reproducibility of shared tasks in general, and PAN's tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.

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“…Linguistic profiling is a behavioural biometric that attempts to identify and discriminate between users based on linguistic morphology [19]. Linguistic profiling was used for determination of the language variety or genre of a text, or a classification for document routing or inform of the text often based upon content words in language compared to average profil dertaken on this technique as lexical patterns, synta throughout a string of text.…”

Section: Linguistic Profilingmentioning

confidence: 99%

“…Linguistic features such as specific aspe n frequency counts of functions words in linguistics and engineering are used as a text profile, which can then les for groups of texts. Considerable research has been and many types of linguistic features can be profiled s x, semantics, information content or item distribut Many researchers concluded that structural and stylom ols for author identification and verification [19][20][21]. idual biometric approaches is that no single biometric rios.…”

Section: Data Collectionmentioning

confidence: 99%

Multi-modal Behavioural Biometric Authentication for Mobile Devices

Saevanee

Clarke

Furnell

2012

IFIP Advances in Information and Communication Technology

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Abstract. The potential advantages of behavioural biometrics are that they can be utilised in a transparent (non-intrusive) and continuous authentication system. However, individual biometric techniques are not suited to all users and scenarios. One way to increase the reliability of transparent and continuous authentication systems is create a multi-modal behavioural biometric authentication system. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that behaviour profiling, keystroke dynamics and linguistic profiling can be used to discriminate users with overall error rates 20%, 20% and 22% respectively. To study the feasibility of multi-modal behaviour biometric authentication system, matching-level fusion methods were applied. Two fusion methods were utilised: simple sum and weight average. The results showed clearly that matching-level fusion can improve the classification performance with an overall EER 8%.

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Linguistic profiling for author recognition and verification

Cited by 86 publications

References 5 publications

Computational methods in authorship attribution

Computational methods in authorship attribution

Improving the Reproducibility of PAN’s Shared Tasks:

Multi-modal Behavioural Biometric Authentication for Mobile Devices

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