A deep learning approach to bilingual lexicon induction in the biomedical domain.


Type
Article
Change log
Authors
Vulić, Ivan 
Moens, Marie-Francine 
Abstract

BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. RESULTS: The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. CONCLUSIONS: Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin).

Description
Keywords
Bilingual lexicon induction, Biomedical text mining, Medical terminology, Representation learning, Data Mining, Deep Learning, Humans, Knowledge Bases, Multilingualism, Natural Language Processing, Semantics
Journal Title
BMC Bioinformatics
Conference Name
Journal ISSN
1471-2105
1471-2105
Volume Title
19
Publisher
Springer Science and Business Media LLC
Sponsorship
European Research Council (648909)