Medical Entity Linking using Triplet Network

Mondal, Ishani; Purkayastha, Sukannya; Sarkar, Sudeshna; Goyal, Pawan; Pillai, Jitesh; Bhattacharyya, Amitava; Gattu, Mahanandeeshwar

doi:10.18653/v1/w19-1912

Cited by 39 publications

(18 citation statements)

References 12 publications

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“…To alleviate the problems of classification-based approaches, researchers apply learning to rank in concept normalization, a two-step framework including a non-trained candidate generator and a supervised candidate ranker that takes both mention and candidate concept as input. Previous candidate rankers have used point-wise learning to rank (Li et al, 2017), pair-wise learning to rank (Leaman et al, 2013;Liu and Xu, 2017;Nguyen et al, 2018;Mondal et al, 2019), and list-wise learning to rank (Murty et al, 2018;Ji et al, 2020;Xu et al, 2020). These learning to rank approaches also have drawbacks.…”

Section: Related Workmentioning

confidence: 99%

“…Online hard triplet mining allows such a vector space model to generate triplets of (mention, true concept, false concept) within a mini-batch, leading to efficient training and fast convergence (Schroff et al, 2015). In contrast with previous vector space models where mention and candidate concepts are mapped to vectors via TF-IDF (Leaman et al, 2013), TreeLSTMs (Liu and Xu, 2017), CNNs (Nguyen et al, 2018;Mondal et al, 2019) or ELMO (Schumacher et al, 2020), we generate vector representations with BERT (Devlin et al, 2019), since it can encode both surface and semantic information (Ma et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…There are a few similar works to our vector space model, CNN-triplet (Mondal et al, 2019), BIOSYN (Sung et al, 2020), RoBERTa-Node2Vec (Pattisapu et al, 2020), and TTI (Henry et al, 2020). CNN-triplet is a two-step approach, requiring a generator to generate candidates for training the triplet network, and requiring various embedding resources as input to CNN-based encoder.…”

Section: Related Workmentioning

confidence: 99%

“…Research on concept normalization has grown thanks to shared tasks such as disorder normalization in the 2013 ShARe/CLEF (Suominen et al, 2013), chemical and disease normalization in BioCreative V Chemical Disease Relation (CDR) Task , and medical concept normalization in 2019 n2c2 shared task (Henry et al, 2020), and to the availability of annotated data (Dogan et al, 2014;Luo et al, 2019). Existing approaches can be divided into three categories: rule-based approaches using string-matching or dictionary look-up (Leal et al, 2015;D'Souza and Ng, 2015;Lee et al, 2016), which rely heavily on handcrafted rules and domain knowledge; supervised multi-class classifiers (Limsopatham and Collier, 2016;Lee et al, 2017;Tutubalina et al, 2018;Niu et al, 2019;Li et al, 2019), which cannot generalize to concept types not present in their training data; and two-step frameworks based on a nontrained candidate generator and a supervised candidate ranker (Leaman et al, 2013;Li et al, 2017;Liu and Xu, 2017;Nguyen et al, 2018;Murty et al, 2018;Mondal et al, 2019;Ji et al, 2020;Xu et al, 2020), which require complex pipelines and fail if the candidate generator does not find the gold truth concept.…”

Section: Introductionmentioning

confidence: 99%

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Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization

Xu¹,

Bethard²

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is critical for mining and analyzing biomedical texts. We propose a vector-space model for concept normalization, where mentions and concepts are encoded via transformer networks that are trained via a triplet objective with online hard triplet mining. The transformer networks refine existing pre-trained models, and the online triplet mining makes training efficient even with hundreds of thousands of concepts by sampling training triples within each mini-batch. We introduce a variety of strategies for searching with the trained vector-space model, including approaches that incorporate domain-specific synonyms at search time with no model retraining. Across five datasets, our models that are trained only once on their corresponding ontologies are within 3 points of state-of-the-art models that are retrained for each new domain. Our models can also be trained for each domain, achieving new state-of-the-art on multiple datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization

Xu¹,

Bethard²

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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“…During token replacement, we need the entire entity to be replaced, but the MLM model (token-level replacement) fails to generate correct synonym of entity fitting in the context. So, we need a BioNER+Entity Linker (Martins et al, 2019), (Mondal et al, 2019) to link entity to ontology for generating correct synonyms.…”

Section: Introductionmentioning

confidence: 99%

BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification

Mondal

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the everincreasing biomedical literature poses unique challenges. We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.

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