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Syntax-based transfer learning for the task of biomedical relation extraction

Joël Legrand 1, 2 Yannick Toussaint 1 Chedy Raïssi 1 Adrien Coulet 1, 3, 4 
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 HeKA - Health data- and model- driven Knowledge Acquisition
Inria de Paris, CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
Abstract : Abstract Background Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. Results In this paper, we experiment with transfer learning for the task of relation extraction from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical relation extraction tasks and equal performances for two others, for which little annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in transfer learning for relation extraction. Conclusion Given the difficulty to manually annotate corpora in the biomedical domain, the proposed transfer learning method offers a promising alternative to achieve good relation extraction performances for domains associated with scarce resources. Also, our analysis illustrates the importance that syntax plays in transfer learning, underlying the importance in this domain to privilege approaches that embed syntactic features.
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Contributor : Adrien Coulet Connect in order to contact the contributor
Submitted on : Wednesday, September 1, 2021 - 11:41:50 AM
Last modification on : Friday, August 5, 2022 - 12:02:36 PM

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Joël Legrand, Yannick Toussaint, Chedy Raïssi, Adrien Coulet. Syntax-based transfer learning for the task of biomedical relation extraction. Journal of Biomedical Semantics, BioMed Central, 2021, 12 (1), ⟨10.1186/s13326-021-00248-y⟩. ⟨hal-03330823⟩



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