The Jinan Chinese Learner Corpus

Wang, Maolin; Malmasi, Shervin; Huang, Mingxuan

doi:10.3115/v1/w15-0614

Cited by 22 publications

(17 citation statements)

References 20 publications

(11 reference statements)

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“…Growing interest has led to the recent development of the Jinan Chinese Learner Corpus (JCLC) (Wang, Malmasi and Huang 2015), the first large-scale corpus of L2 Chinese consisting of university student essays. Learners from fifty-nine countries are represented and proficiency levels are sampled representatively across beginner, intermediate and advanced levels.…”

Section: Chinesementioning

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: Chinesementioning

confidence: 99%

Multilingual native language identification

Malmasi¹,

Dras²

2015

Nat. Lang. Eng.

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“…The best result under crossvalidation on the TOEFL dataset, which includes 11 native languages (with a rather diverse distribution of language families), was 85.2% accuracy. Applying these methods to different datasets (the ASK corpus of learners of Norwegian (Tenfjord et al, 2006) and the Jinan Chinese Learner Corpus (Wang et al, 2015), 10-11 native languages in each) resulted in 76.5% accuracy for the Chinese data and 81.8% for the Norwegian data, with LDA-based classification yielding top results.…”

Section: Related Workmentioning

confidence: 99%

Native Language Identification with User Generated Content

Goldin¹,

Rabinovich²,

Wintner³

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.

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“…Recent NLI studies on languages other than English include Arabic (Malmasi and Dras, 2014a) and Chinese (Malmasi and Dras, 2014b;Wang et al, 2015). To the best of our knowledge, no study has been published on Portuguese and the NLI-PT dataset opens new possibilities of research for Portuguese.…”

Section: Related Workmentioning

confidence: 99%

“…Even though most NLI research has been carried out on English data, an important research trend in recent years has been the application of NLI methods to other languages, as discussed in Malmasi and Dras (2015). Recent NLI studies on languages other than English include Arabic (Malmasi and Dras, 2014a) and Chinese (Malmasi and Dras, 2014b;Wang et al, 2015). To the best of our knowledge, no study has been published on Portuguese and the NLI-PT dataset opens new possibilities of research for Portuguese.…”

Section: Related Workmentioning

confidence: 99%

A Portuguese Native Language Identification Dataset

Gayo

Zampieri

Malmasi

2018

Proceedings of the Thirteenth Workshop on Innovative Use of NLP For Building Educational Applications

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In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. We discuss possible applications of this dataset and present the results obtained for the first lexical baseline system for Portuguese NLI.

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The Jinan Chinese Learner Corpus

Cited by 22 publications

References 20 publications

Multilingual native language identification

Multilingual native language identification

Native Language Identification with User Generated Content

A Portuguese Native Language Identification Dataset

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