Machine translation and author keywords: A viable search strategy for scholars with limited English proficiency?

Authors

  • Lynne Bowker University of Ottawa

DOI:

https://doi.org/10.7152/acro.v29i1.15455

Keywords:

Machine translation, author keywords, information retrieval, knowledge organization systems

Abstract

Author keywords are valuable for indexing articles and for information retrieval (IR). Most scientific literature is published in English. Can machine translation (MT) help researchers with limited English proficiency to search for information? We used two MT systems (Google Translate, DeepL Translator) to translate into English 71 Spanish keywords and 43 French keywords from articles in the domain of Library and Information Science. We then used the English translations to search the Library, Information Science and Technology Abstracts (LISTA) database. Half of the translated keywords returned relevant results. Of the half that did not, 34% were well translated but did not align with LISTA descriptors. Translation-related problems stemming from orthographic variation, synonymy, differing syntactic preferences, and semantic field coverage interfered with IR in just 16% of cases. Some of the MT errors are relatively “predictable” and if knowledge organization systems could be augmented to deal with them, then MT may prove even more useful for searching.

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Published

2019-06-28

Issue

29th ASIS SIG/CR Classification Research Workshop

Section

Articles

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