This paper describes a method of detecting grammatical and lexical errors made by Japanese learners of English and other techniques that improve the accuracy of error detection with a limited amount of training data. In this paper, we demonstrate to what extent the proposed methods hold promise by conducting experiments using our learner corpus, which contains information on learners' errors.

Investigation into Japanese learners'acquisition order of major grammatical morphemes using error-tagged learner corpus EMI IZUMI•õ,KOYOTAKA UCHIMOTO•õ nd HITOSHI ISAHARA•õ In foreign language education,it is important for teachers to know their students' acquisition order of major linguistic items in the target language.There have been a lot of studies done for revealing natural sequence in second language acquisition since 1970's,and it is one of the established ideas that major grammatical morphemes are acquired in the common order by learners across different backgrounds such as their L1,ages,or learning environments(The fist hypothesis).

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Language learners use various kinds of communication strategies to compensate their imperfect knowledge on the target language,especially vocabulary.Paraphrase is one of the important strategies.It must be worth doing to teach how paraphrase in foreign language communication can be done more effectively.In vocabulary teaching, if the certain word entry is introduced with several other relevant words or expressions,it would be quite helpful for learners to do more successful paraphrase.In this research,we have done the experiment to see how this kind of expanded teaching of vocabulary or expressions can be accomplished by using diverse expressions extracted from the spoken corpus of Japanese learner English,"The NICT JLE (Japanese Learner English)Corpus".In the experiment,we first made the keyword list based on the small amount of English native speakers' utterances by hand. Then,we automatically extracted the diverse expressions which describe the particular matter from the learner data to make the learners' expression list.We have

Analysis of publicly available language learning corpora can be useful for extracting characteristic features of learners from different proficiency levels. This can then be used to support language learning research and the creation of educational resources. In this paper, we classify the words and parts of speech of transcripts from different speaking proficiency levels found in the NICT-JLE corpus. The characteristic features of learners who have the equivalent spoken proficiency of CEFR levels A1 through to B2 were extracted by analyzing the data with the support vector machine method. In particular, we apply feature selection to find a set of characteristic features that achieve optimal classification performance, which can be used to predict spoken learner proficiency.