Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations

Pieter Fivez, Simon Suster, Walter Daelemans


Abstract
Effective representation of biomedical names for downstream NLP tasks requires the encoding of both lexical as well as domain-specific semantic information. Ideally, the synonymy and semantic relatedness of names should be consistently reflected by their closeness in an embedding space. To achieve such robustness, prior research has considered multi-task objectives when training neural encoders. In this paper, we take a next step towards truly robust representations, which capture more domain-specific semantics while remaining universally applicable across different biomedical corpora and domains. To this end, we use conceptual grounding constraints which more effectively align encoded names to pretrained embeddings of their concept identifiers. These constraints are effective even when using a Deep Averaging Network, a simple feedforward encoding architecture that allows for scaling to large corpora while remaining sufficiently expressive. We empirically validate our approach using multiple tasks and benchmarks, which assess both literal synonymy as well as more general semantic relatedness.
Anthology ID:
2021.eacl-main.208
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2440–2450
Language:
URL:
https://aclanthology.org/2021.eacl-main.208
DOI:
10.18653/v1/2021.eacl-main.208
Bibkey:
Cite (ACL):
Pieter Fivez, Simon Suster, and Walter Daelemans. 2021. Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2440–2450, Online. Association for Computational Linguistics.
Cite (Informal):
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations (Fivez et al., EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.eacl-main.208.pdf
Code
 clips/conceptualgrounding
Data
MedMentions