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Automatic Translation, Context, and Supervised Learning in Comparative Politics
Courtney, Michael; Breen, Michael; McMenamin, Iain; McNulty, Gemma
This paper proves that automatic translation of multilingual newspaper documents deters neither human nor computer classification of political concepts. We show how theory-driven coding of newspaper text can be automated in several languages by monolingual researchers. Supervised machine learning is successfully applied to text in English from British, Spanish and German sources. The paper has three main findings. First, results from human coding directly in a foreign language do not differ from coding computer-translated text. Second, humans can code translated text as well as they can code untranslated prose in their mother tongue. Third, machine learning based on translated Spanish and German training sets can reproduce human coding as accurately as a system learning from English training sets.
Keyword(s): Machine learning; Machine translating; Translating and interpreting; International relations; Mass media; Political science; automatic translation; supervised learning; google translate; media; newspapers; comparative politics; text analysis; political text
Publication Date:
2020
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Courtney, Michael, Breen, Michael ORCID: 0000-0002-5857-9938 <https://orcid.org/0000-0002-5857-9938>, McMenamin, Iain ORCID: 0000-0002-1704-390X <https://orcid.org/0000-0002-1704-390X> and McNulty, Gemma (2020) Automatic Translation, Context, and Supervised Learning in Comparative Politics. Journal of Information Technology and Politics . ISSN 1933-1681
Publisher(s): Taylor & Francis
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/24233/1/WITP_A_1731245.pdf,
http://dx.doi.org/10.1080/19331681.2020.1731245
First Indexed: 2020-02-22 07:06:44 Last Updated: 2020-02-22 07:06:44