Mining revision log of language learning SNS for automated Japanese error correction

Tomoya Mizumoto, Mamoru Komachi, Masaaki Nagata, Yuji Matsumoto

研究成果: Article査読

1 被引用数 (Scopus)


Recently, natural language processing research has begun to pay attention to second language learning. However, it is not easy to acquire a large-scale learners' corpus, which is important for a research for second language learning by natural language processing. We present an attempt to extract a large-scale Japanese learners' corpus from the revision log of a language learning social network service.This corpus is easy to obtain in large-scale, covers a wide variety of topics and styles, and can be a great source of knowledge for both language learners and instructors. We also demonstrate that the extracted learners' corpus of Japanese as a second language can be used as training data for learners' error correction using a statistical machine translation approach.We evaluate different granularities of tokenization to alleviate the problem of word segmentation errors caused by erroneous input from language learners.We propose a character-based SMT approach to alleviate the problem of er oneous input from language learners.Experimental results show that the character-based model outperforms the word-based model when corpus size is small and test data is written by the learners whose L1 is English.

ジャーナルTransactions of the Japanese Society for Artificial Intelligence
出版ステータスPublished - 2013 7 10

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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