A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017

@inproceedings{Oh2017ADB,
  title={A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017},
  author={Yoo Rhee Oh and Hyung-Bae Jeon and Hwa Jeon Song and Yun-Kyung Lee and Jeon Gue Park and Yun-Keun Lee},
  booktitle={BEA@EMNLP},
  year={2017}
}
This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 (Malmasi et al., 2017) where the NLI Shared Task 2017 aims to detect the native language of an essay or speech response of a standardized assessment of English proficiency for academic purposes. To this end, we use the six unit forms of a text data such as character 4/5/6-grams and word 1/2/3-grams. For each unit form of… CONTINUE READING

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Key Quantitative Results

  • From the NLI experiments, the F1 (macro) scores are obtained as 0.8601, 0.8664, and 0.9220 for the essay track, the speech track, and the fusion track, respectively.

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