Predicting Author’s Native Language Using Abstracts of Scholarly Papers
| Autor: | Takahiro Baba, Kensuke Baba, Daisuke Ikeda |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Native-language identification
Computer science First language Document classification 02 engineering and technology computer.software_genre Linguistics Writing style 03 medical and health sciences 0302 clinical medicine Binary classification 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer Word (computer architecture) |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030018504 ISMIS |
| Popis: | Predicting author’s attributes is useful for understanding implicit meanings of documents. The target problem of this paper is predicting author’s native language for each document. The authors of this paper used surface-level features of documents for the problem and tried to clarify the practical tendencies of the writing style as word occurrences. They conducted a classification of the abstracts written in English of approximately 85,000 scholarly papers written in English or in Japanese. As a result of the experiment, the accuracy of the binary classification was 0.97, and they found that a number of distinctive phrases used in the classification were related to typical writing styles of Japanese. |
| Databáze: | OpenAIRE |
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