Determining an author's native language by mining a text for errors

Koppel, Moshe; Schler, Jonathan; Zigdon, Kfir

doi:10.1145/1081870.1081947

Cited by 101 publications

(129 citation statements)

References 8 publications

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“…Many studies since that of Mosteller and Wallace have shown the efficacy of function words for authorship attribution in different scenarios (Morton 1978;Burrows 1987;Karlgren & Cutting 1994;Merriam & Matthews 1994;Kessler et al 1997;Argamon et al 1998;Holmes 1998;de Vel et al 2001;Holmes et al 2001aHolmes et al , 2001bBaayen et al 2002;Binongo 2003;Juola & Baayen 2003;Zhao & Zobel 2005;Argamon & Levitan 2005;Koppel et al 2005Koppel et al , 2006a, confirming the hypothesis that different authors tend to have different characteristic patterns of function word use.…”

Section: Function Wordsmentioning

confidence: 82%

“…More recently, Graham et al (2005) and Zheng et al (2006) used neural networks on a wide variety of features. Other studies used k-nearest neighbor (Kjell et al 1995;Hoorn et al 1999;Zhao & Zobel 2005), Naive Bayes (Kjell 1994a;Hoorn et al 1999;Peng et al 2004), rule learners (Holmes & Forsyth 1995;Holmes 1998;Argamon et al 1998;Koppel & Schler 2003;Abbasi & Chen 2005;Zheng et al 2006), support vector machines (De Vel et al 2001;Diederich et al 2003;Koppel & Schler 2003, Abbasi & Chen 2005Koppel et al 2005;Zheng et al 2006), Winnow (Koppel et al 2002;Argamon et al 2003;Koppel et al 2006a), and Bayesian regression Madigan et al 2006;Argamon et al 2008). Further details regarding these studies can be found in the Appendix.…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…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%

“…As in Argamon et al (2008), we consider the following profile dimensions: author gender (Koppel et al 2002;Argamon et al 2003), age (Burger and Henderson 2006;Schler et al 2006), native language (Koppel et al 2005) and neuroticism level (Pennebaker & King 1999;Pennebaker, Mehl, & Niederhoffer, 2003). For each of these, we assemble an appropriately labeled corpus and proceed exactly as described above.…”

Section: Profilingmentioning

confidence: 99%

“…Elsewhere (Koppel et al 2005), we have shown that for determining native language, features that measure stylistic idiosyncrasies and errors are particularly useful. Using such features together with the style features considered in this section yields classification accuracy of over 80% for this task.…”

Section: Native Languagementioning

confidence: 99%

See 4 more Smart Citations

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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Section: Function Wordsmentioning

confidence: 82%

Section: Machine Learning Approachmentioning

confidence: 99%

Section: Syntax and Parts-of-speechmentioning

confidence: 99%

Section: Profilingmentioning

confidence: 99%

Section: Native Languagementioning

confidence: 99%

See 3 more Smart Citations

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

Self Cite

452

278

View full text Add to dashboard Cite

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Using psycholinguistic features for profiling first language of authors

Torney

Vamplew

Yearwood

2012

J Am Soc Inf Sci Tec

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This study empirically evaluates the effectiveness of different feature types for the classification of the first language of an author. In particular, it examines the utility of psycholinguistic features, extracted by the Linguistic Inquiry and Word Count (LIWC) tool, that have not previously been applied to the task of author profiling. As LIWC is a tool that has been developed in the psycholinguistic field rather than the computational linguistics field, it was hypothesized that it would be effective, both as a single type feature set because of its psycholinguistic basis, and in combination with other feature sets, because it should be sufficiently different to add insight rather than redundancy. It was found that LIWC features were competitive with previously used feature types in identifying the first language of an author, and that combined feature sets including LIWC features consistently showed better accuracy rates and average F measures than were achieved by the same feature sets without the LIWC features. As a secondary issue, this study also examined how effectively first language classification scaled up to a larger number of possible languages. It was found that the classification scheme scaled up effectively to the entire 16 language collection from the International Corpus of Learner English, when compared with results achieved on just 5 languages in previous research.

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