Bilingual dictionary generation and enrichment via graph exploration

Tracking #: 2899-4113

This paper is currently under review
Shashwat Goel
Jorge Gracia
Mikel Lorenzo Forcada

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Guest Editors Advancements in Linguistics Linked Data 2021

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In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data has stimulated the development and use of openly available linguistic knowledge graphs, as it is the case or Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speedup, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. On average over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as a free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.
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