Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Europe PMC: Europe PubMed Central. https://europepmc.org/. Accessed 5 Feb 2018
Oxford Dictionary of English. Oxford University Press (2010)
Berzak, Y., Nakamura, C., Flynn, S., Katz, B.: Predicting native language from gaze. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 541–551 (2017)
Ionescu, R.T., Popescu, M., Cahill, A.: String kernels for native language identification: insights from behind the curtains. Comput. Linguist. 42(3), 491–525 (2016)
Paquette, G.: English Composition for Scholarly Works (in Japanese). Kyoto University Press, Kyoto (2004)
Wong, S.-M.J., Dras, M.: Exploiting parse structures for native language identification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1600–1610. Association for Computational Linguistics, Stroudsburg (2011)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 15H02787.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Baba, T., Baba, K., Ikeda, D. (2018). Predicting Author’s Native Language Using Abstracts of Scholarly Papers. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-01851-1_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01850-4
Online ISBN: 978-3-030-01851-1
eBook Packages: Computer ScienceComputer Science (R0)