Title :
Classification and Clustering English Writing Errors Based on Native Language
Author :
Flanagan, Brendan ; Chengjiu Yin ; Suzuki, Takumi ; Hirokawa, Sachio
Author_Institution :
Grad. Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan
fDate :
Aug. 31 2014-Sept. 4 2014
Abstract :
It is important for language learners to determine and reflect on their writing errors in order to overcome weaknesses. Each language learner has their own unique writing error characteristics and therefore has different learning needs. In this paper, we analyze the writing errors of foreign language learners on the language learning SNS website Lang-8 to investigate the characteristics of errors by native language. 142,465 sentences were collected from Lang-8 for analysis. For each native language, the predicted scores of 15 error categories from SVM machine learning models are used as a vector representation of each sentence. These score vectors are then clustered to determine error co-occurrence within the same sentence. The results were then analyzed to determine the error characteristics of different native languages.
Keywords :
computer aided instruction; learning (artificial intelligence); natural language processing; pattern classification; pattern clustering; social networking (online); support vector machines; vectors; English writing errors; Lang-8; SNS Web site; SVM; classification; clustering; error characteristics; machine learning; native language; vector representation; Data models; Educational institutions; Principal component analysis; Support vector machines; Vectors; Writing; Language learning; machine learning; native language characteristics; writing error categories;
Conference_Titel :
Advanced Applied Informatics (IIAIAAI), 2014 IIAI 3rd International Conference on
Conference_Location :
Kitakyushu
Print_ISBN :
978-1-4799-4174-2
DOI :
10.1109/IIAI-AAI.2014.72
