CIC-FBK Approach to Native Language Identification

Markov, Ilia; Chen, Lingzhen; Strapparava, Carlo; Sidorov, Grigori

doi:10.18653/v1/w17-5042

Cited by 19 publications

(13 citation statements)

References 17 publications

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“…Their experiments indicate that inclusion of the sentence prediction features provides a small increase in performance. (Markov et al, 2017) build an SVM with multiple lexical and syntactic features. They introduce two new feature types -typed character n-grams and syntactic n-grams -and combine them with word, lemma, and POS n-grams, function words, and spelling error character n-grams.…”

Section: Essay-only Trackmentioning

confidence: 99%

A Report on the 2017 Native Language Identification Shared Task

Tetreault¹,

Blanchard²,

Cahill³

2017

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

112

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Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features.

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Section: Essay-only Trackmentioning

confidence: 99%

A Report on the 2017 Native Language Identification Shared Task

Tetreault¹,

Blanchard²,

Cahill³

2017

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

112

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“…The first classifier based on logistic regression works at the sentences level; results are provided to the second classifier which uses the support vector method and already works at the level of the whole text. The second result (88.08%) was shown by the CIC-FBK team [Markov et al, 2017] also used the support vector machine based on the standard set of features, such as symbolic, vocabulary and POS n-grams, functional words. In addition to these features, several new features including syntactic n-grams were also used.…”

Section: Related Workmentioning

confidence: 97%

Native Language Identification for Russian Using Errors Types

Remnev¹

2020

Computational Linguistics and Intellectual Technologies

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The task of recognizing the author’s native (Native Language Identification—NLI) language based on a texts, written in a language that is non-native to the author—is the task of automatically recognizing native language (L1). The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 and 2017, where TOEFL English essays and essay samples were used as data. There is also a small number of works where the NLI problem was solved for other languages. The NLI problem was investigated for Russian by Ladygina (2017) and Remnev (2019). This paper discusses the use of well-established approaches in the NLI Shared Task 2013 and 2017 competitions to solve the problem of recognizing the author’s native language, as well as to recognize the type of speaker—learners of Russian or Heritage Russian speakers. Native language identification task is also solved based on the types of errors specific to different languages. This study is data-driven and is possible thanks to the Russian Learner Corpus developed by the Higher School of Economics (HSE) Learner Russian Research Group on the basis of which experiments are being conducted.

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“…3.3.1 Part-of-speech tags and function words POS features capture the morpho-syntactic patterns in a text, and are indicative of the L1, especially when used in combination with other types of features (Cimino and Dell'Orletta, 2017;Markov et al, 2017). POS tags were obtained with TreeTagger (Schmid, 1999), which uses the Penn Treebank tagset (36 tags).…”

Section: Featuresmentioning

confidence: 99%

Anglicized Words and Misspelled Cognates in Native Language Identification

Markov¹,

Năstase²,

Strapparava³

2019

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

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In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2). In particular, we look at misspelled words that indicate lexical uncertainty on the part of the author, and separate them into three categories: misspelled cognates, "L2-ed" (in our case, anglicized) words, and all other spelling errors. We test the assumption that such errors contain clues about the native language of an essay's author through the task of native language identification. The results of the experiments show that the information brought by each of these categories is complementary. We also note that while the distribution of such features changes with the proficiency level of the writer, their contribution towards native language identification remains significant at all levels.

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CIC-FBK Approach to Native Language Identification

Cited by 19 publications

References 17 publications

A Report on the 2017 Native Language Identification Shared Task

A Report on the 2017 Native Language Identification Shared Task

Native Language Identification for Russian Using Errors Types

Anglicized Words and Misspelled Cognates in Native Language Identification

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