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• W2250248908 abstract "Native language identification (NLI) is the task of determining the native language of an author writing in a second language. Several pieces of earlier work have found that features such as function words, part-of-speech n-grams and syntactic structure are helpful in NLI, perhaps representing characteristic errors of different native language speakers. This paper looks at the idea of using Latent Dirichlet Allocation as a feature clustering technique over lexical features to see whether there is any evidence that these smaller-scale features do cluster into more coherent latent factors, and investigates their effect in a classification task. We find that although (not unexpectedly) classification accuracy decreases, there is some evidence of coherent clustering, which could help with much larger syntactic feature spaces." @default.
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• W2250248908 date "2011-12-01" @default.
• W2250248908 modified "2023-09-24" @default.
• W2250248908 title "Topic Modeling for Native Language Identification" @default.
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