Chinese Native Language Identification

Malmasi, Shervin; Dras, Mark

doi:10.3115/v1/e14-4019

Cited by 32 publications

(30 citation statements)

References 7 publications

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“…This would enable them to not just provide new evidence for previous findings, but to also perform semi-automated data-driven generation of new and viable hypotheses. This, in turn, can help reduce expert effort and involvement in the process, particularly as such studies expand to more corpora and emerging language like Chinese (Malmasi and Dras, 2014b) and Arabic (Malmasi and Dras, 2014a).…”

Section: Discussionmentioning

confidence: 99%

Language Transfer Hypotheses with Linear SVM Weights

Malmasi¹,

Dras²

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Language transfer, the characteristic second language usage patterns caused by native language interference, is investigated by Second Language Acquisition (SLA) researchers seeking to find overused and underused linguistic features. In this paper we develop and present a methodology for deriving ranked lists of such features. Using very large learner data, we show our method's ability to find relevant candidates using sophisticated linguistic features. To illustrate its applicability to SLA research, we formulate plausible language transfer hypotheses supported by current evidence. This is the first work to extend Native Language Identification to a broader linguistic interpretation of learner data and address the automatic extraction of underused features on a per-native language basis.

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Section: Discussionmentioning

confidence: 99%

Language Transfer Hypotheses with Linear SVM Weights

Malmasi¹,

Dras²

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Analyzing the language produced by learners could provide insight into the limitations of learners' vocabulary. Learner corpora, widely used in the task of Native Language Identification (Malmasi and Dras, 2014;Malmasi and Dras, 2015b) could be useful here.…”

Section: Resultsmentioning

confidence: 99%

MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier

Malmasi¹,

Zampieri²

2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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This paper describes team MAZA entries for the 2016 SemEval Task 11: Complex Word Identification (CWI). The task is a binary classification task in which systems are trained to predict whether a word in a sentence is considered to be complex or not. We developed our two systems for this task based on classifier stacking using decision stumps and decision trees. Our best system, using contextual features, frequency information, and word and sentence length, achieved 91.2% accuracy and 30.8% F-Score. The system ranked 4 th among the 38 entries in the CWI task in terms of FScore.

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“…This can provide useful information in forensic linguistic tasks (Estival et al, 2007) or could be used in an educational setting to provide contrastive feedback to language learners. Most research has focused on identifying the native language of English language learners, though there have been some efforts recently to identify the native language of writing in other languages (Malmasi and Dras, 2014).…”

Section: Native Language Identificationmentioning

confidence: 99%

Can characters reveal your native language? A language-independent approach to native language identification

Ionescu¹,

Popescu²,

Cahill³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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A common approach in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. In this work, an approach that uses character n-grams as features is proposed for the task of native language identification. Instead of doing standard feature selection, the proposed approach combines several string kernels using multiple kernel learning. Kernel Ridge Regression and Kernel Discriminant Analysis are independently used in the learning stage. The empirical results obtained in all the experiments conducted in this work indicate that the proposed approach achieves state of the art performance in native language identification, reaching an accuracy that is 1.7% above the top scoring system of the 2013 NLI Shared Task. Furthermore, the proposed approach has an important advantage in that it is language independent and linguistic theory neutral. In the cross-corpus experiment, the proposed approach shows that it can also be topic independent, improving the state of the art system by 32.3%.

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Chinese Native Language Identification

Cited by 32 publications

References 7 publications

Language Transfer Hypotheses with Linear SVM Weights

Language Transfer Hypotheses with Linear SVM Weights

MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier

Can characters reveal your native language? A language-independent approach to native language identification

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