Biomedical Concept Normalization ( BCN ) is widely used in biomedical text processing as a fundamental module . Owing to numerous surface variants of biomedical concepts , BCN still remains challenging and unsolved . In this paper , we exploit biomedical concept hypernyms to facilitate BCN . We propose Biomedical Concept Normalizer with Hypernyms ( BCNH ) , a novel framework that adopts list-wise training to make use of both hypernyms and synonyms , and also employs norm constraint on the representation of hypernym-hyponym entity pairs . The experimental results show that BCNH outperform the previous state-of-the-art model on the NCBI dataset . Code will be available at https://github.com/ yan-cheng / BCNH . Biomedical Concept Normalization ( BCN ) plays an important and prerequisite role in biomedical text processing . The goal of BCN is to link the entity mention in the context to its normalized CUI ( Unique Concept Identifier ) in the biomedical dictionaries such as UMLS ( Bodenreider , 2004 ) , SNOMED-CT ( Spackman et al . , 1997 ) and MedDRA ( Brown et al . , 1999 ) . Figure 1 is an example of BCN from NCBI dataset ( Dogan et al . , 2014 ) , the mention B-cell non-Hodgkins lymphomas should be linked to D016393 Lymphoma , B-Cell in the MEDIC ( Davis et al . , 2012 ) dictionary . Recent works on BCN usually adopt encoders like CNN ( Li et al . , 2017 ) , LSTM ( Phan et al . , 2019 ) , ELMo ( Peters et al . , 2018 ; Schumacher et al . , 2020 ) or BioBERT ( Lee et al . , 2020 ; Fakhraei et al . , 2019 ; Ji et al . , 2020 ) to embed both the mention and the concept 's name entities , and then feed the representations to the following classifier or ranking network to determine the corresponding concept in the biomedical dictionary . However , biomedical dictionaries are generally sparse in nature : a concept is usually provided with only CUI , referred name ( recommended concept name string ) , synonyms ( acceptable name variants , synonyms ) , and related concepts ( mainly hypernym concepts ) . Therefore , effectively using the limited information in the biomedical dictionary where the candidate entities came from is paramount for the BCN task . For concept 's synonym entities , recent BNE ( Phan et al . , 2019 ) and BIOSYN ( Sung et al . , 2020 ) tries to make full use of them by synonym marginalization to enhance biomedical entity representation and achieved consistent performance improvement . Unfortunately , previous works generally ignore concept hypernym hierarchy structure , which is exactly the initial motivation of biomedical dictionary : organization of thousands of concepts under a unified and multi-level hierarchical classification schema . We believe that leveraging hypernym information in the biomedical dictionary can improve the BCN performance based on two intuitions . First , hard negative sampling ( Fakhraei et al . , 2019 ; Phan et al . , 2019 ) is vital for the BCN model 's discriminating ability and a hypernym is a hard negative example for its hyponym naturally . Second , injecting the hypernym hierarchy information during the training process is beneficial for encoders , since currently used encoders like BioBERT only encodes the context semantics in biomedical corpora instead of the biomedical concept structural information . To this end , we propose Biomedical Concept Normalizer with Hypernyms ( BCNH ) , a novel framework combining the list-wise cross entropy loss with norm constraint on hypernym-hyponym entity pairs . Concretely , we reformulate the candidate target list as a three-level relevance list to consider both synonyms and hypernyms , and apply the list-wise cross entropy loss . On the one hand , synonyms help to encode surface name variants , on the other hand , hypernyms help encode hierarchical structural information . We also apply the norm constraint on the embedding of hypernym-hyponym entity pairs to further preserve the principal hypernym relation . Specifically , for a hypernym-hyponym entity pair ( e hyper , e hypo ) , we constraint that the norm of hypernym entity e hyper is larger than that of e hyper in a multi-task manner . We conduct experiments on the NCBI dataset and outperforms the previous state-of-the-art model . To sum up , the contributions of this paper are as follows . First , for the first time , we reformulate the candidate target list as a three-level relevance list and apply the list-wise loss to attend all candidate entities . Second , we innovatively use norm constraint to model the hypernym-hyponym relation , preserving the hierarchy structure information inside the entity representation . The proposed BCNH outperforms the previous state-of-the-art model on the NCBI dataset , leading to an improvement of 0.73 % on top1 accuracy . In this paper , we propose BCNH to leverage hypernyms in the biomedical concept normalization task . We adopts both list-wise training and norm constraint with the help of hypernym information . The experimental results on the NCBI dataset show that BCNH outperforms previous state-of-the-art models .

Challenge: Previous works on the Biomedical Concept Normalization that link the entity mentions in the context to its normalized dictionaries ignore the concept hypernym hierarchy structure.
Approach: They propose to adopt list-wise training to make use of both hypernyms and synonyms and also employ norm constraints on representations of hypernym-synonym entity pairs.
Outcome: The proposed method outperforms the state-of-the-art model on the NCBI dataset by 0.73% on top 1 accuracy.