@inproceedings{ac5efffc048444aa9033958f8f4bdea3,
title = "Predicting author{\textquoteright}s native language using abstracts of scholarly papers",
abstract = "Predicting author{\textquoteright}s attributes is useful for understanding implicit meanings of documents. The target problem of this paper is predicting author{\textquoteright}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.",
keywords = "Document classification, Machine learning, Native language identification, Text analysis",
author = "Takahiro Baba and Kensuke Baba and Daisuke Ikeda",
note = "Funding Information: supported by JSPS KAKENHI Grant Number Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 24th International Symposium on Methodologies for Intelligent Systems, ISMIS 2018 ; Conference date: 29-10-2018 Through 31-10-2018",
year = "2018",
doi = "10.1007/978-3-030-01851-1_43",
language = "English",
isbn = "9783030018504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "448--453",
editor = "Nathalie Japkowicz and Papadopoulos, {George A.} and Michelangelo Ceci and Ras, {Zbigniew W.} and Jiming Liu",
booktitle = "Foundations of Intelligent Systems - 24th International Symposium, ISMIS 2018, Proceedings",
}