Automatically Determining an Anonymous Author’s Native Language

Koppel, Moshe; Schler, Jonathan; Zigdon, Kfir

doi:10.1007/11427995_17

Cited by 44 publications

(35 citation statements)

References 8 publications

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“…However, many of the early and influential NLI studies (e.g. Koppel et al (2005) cumstances. This issue was noted at the time, but did not deter researchers as corpora with similar issues were used for many years.…”

Section: Discussionmentioning

confidence: 99%

Chinese Native Language Identification

Malmasi¹,

Dras²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Volume 2: Short Pa

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We present the first application of Native Language Identification (NLI) to nonEnglish data. Motivated by theories of language transfer, NLI is the task of identifying a writer's native language (L1) based on their writings in a second language (the L2). An NLI system was applied to Chinese learner texts using topicindependent syntactic models to assess their accuracy. We find that models using part-of-speech tags, context-free grammar production rules and function words are highly effective, achieving a maximum accuracy of 71% . Interestingly, we also find that when applied to equivalent English data, the model performance is almost identical. This finding suggests a systematic pattern of cross-linguistic transfer may exist, where the degree of transfer is independent of the L1 and L2.

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Section: Discussionmentioning

confidence: 99%

Chinese Native Language Identification

Malmasi¹,

Dras²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Volume 2: Short Pa

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show abstract

“…Studies in the field of Native Language Identification (NLI) can be considered as relevant to ours, with Koppel et al (2005) being the first to infer the native language of an author based on texts written in a second language by using various NLP and Second Language Acquisition features. The studies in this topic that followed implemented different methods and characteristics for the identification of the author's native language by using various feature types like syntactic clues and grammars (Wong and Dras, 2011;Wong et al, 2012) or different resources and evaluation techniques (Tetreault et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Identifying the Authors’ National Variety of English in Social Media Texts

Simaki¹,

Simakis²,

Paradis³

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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In this paper, we present a study for the identification of authors' national variety of English in texts from social media. In data from Facebook and Twitter, information about the author's social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and databased features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32%. The experimental results are evaluated, and the efficacy of the ranked features discussed.

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“…Part of speech n-grams Koppel et al (2005) found rare part of speech (POS) bigrams to be a useful feature; many teams in the 2013 Shared Task also made use of this feature . We use the Stanford Tagger to extract POS features (Toutanova et al, 2003).…”

Section: Featuresmentioning

confidence: 99%

“…Function words Function words are a closed class of words that serve a grammatical function in sentences, whose use for NLI was explored early on (Koppel et al, 2005). These include articles, determiners, conjunctions, and auxiliaries.…”

Section: Featuresmentioning

confidence: 99%

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Ensemble Methods for Native Language Identification

Chan

Jahromi²,

Benetti³

et al. 2017

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

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Our team-Uvic-NLP-explored and evaluated a variety of lexical features for Native Language Identification (NLI) within the framework of ensemble methods. Using a subset of the highestperforming features, we train Support Vector Machines (SVM) and Fully Connected Neural Networks (FCNN) as base classifiers, and test different methods for combining their outputs. Restricting our scope to the closed essay track in the NLI Shared Task 2017, we find that our best SVM ensemble achieves an F1 score of 0.8730 on the test set.

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Automatically Determining an Anonymous Author’s Native Language

Cited by 44 publications

References 8 publications

Chinese Native Language Identification

Chinese Native Language Identification

Identifying the Authors’ National Variety of English in Social Media Texts

Ensemble Methods for Native Language Identification

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