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    <abstract>Neural encoders of biomedical names are typically considered robust if representations can be effectively exploited for various downstream NLP tasks. To achieve this, encoders need to model domain-specific biomedical semantics while rivaling the universal applicability of pretrained self-supervised representations. Previous work on robust representations has focused on learning low-level distinctions between names of fine-grained biomedical concepts. These fine-grained concepts can also be clustered together to reflect higher-level, more general semantic distinctions, such as grouping the names nettle sting and tick-borne fever together under the description puncture wound of skin. It has not yet been empirically confirmed that training biomedical name encoders on fine-grained distinctions automatically leads to bottom-up encoding of such higher-level semantics. In this paper, we show that this bottom-up effect exists, but that it is still relatively limited. As a solution, we propose a scalable multi-task training regime for biomedical name encoders which can also learn robust representations using only higher-level semantic classes. These representations can generalise both bottom-up as well as top-down among various semantic hierarchies. Moreover, we show how they can be used out-of-the-box for improved unsupervised detection of hypernyms, while retaining robust performance on various semantic relatedness benchmarks.</abstract>
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%0 Conference Proceedings
%T Integrating Higher-Level Semantics into Robust Biomedical Name Representations
%A Fivez, Pieter
%A Suster, Simon
%A Daelemans, Walter
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C online
%F fivez-etal-2021-integrating
%X Neural encoders of biomedical names are typically considered robust if representations can be effectively exploited for various downstream NLP tasks. To achieve this, encoders need to model domain-specific biomedical semantics while rivaling the universal applicability of pretrained self-supervised representations. Previous work on robust representations has focused on learning low-level distinctions between names of fine-grained biomedical concepts. These fine-grained concepts can also be clustered together to reflect higher-level, more general semantic distinctions, such as grouping the names nettle sting and tick-borne fever together under the description puncture wound of skin. It has not yet been empirically confirmed that training biomedical name encoders on fine-grained distinctions automatically leads to bottom-up encoding of such higher-level semantics. In this paper, we show that this bottom-up effect exists, but that it is still relatively limited. As a solution, we propose a scalable multi-task training regime for biomedical name encoders which can also learn robust representations using only higher-level semantic classes. These representations can generalise both bottom-up as well as top-down among various semantic hierarchies. Moreover, we show how they can be used out-of-the-box for improved unsupervised detection of hypernyms, while retaining robust performance on various semantic relatedness benchmarks.
%U https://aclanthology.org/2021.louhi-1.6/
%P 49-58

Markdown (Informal)

[Integrating Higher-Level Semantics into Robust Biomedical Name Representations](https://aclanthology.org/2021.louhi-1.6/) (Fivez et al., Louhi 2021)

• Integrating Higher-Level Semantics into Robust Biomedical Name Representations (Fivez et al., Louhi 2021)

ACL

• Pieter Fivez, Simon Suster, and Walter Daelemans. 2021. Integrating Higher-Level Semantics into Robust Biomedical Name Representations. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pages 49–58, online. Association for Computational Linguistics.