| Challenge: | The aim of this paper is to present our approach in the PharmacoNER 2019 task. |
| Approach: | They propose to use a Bi-LSTM with a CRF to identify named entities from clinical case studies written in Spanish. |
| Outcome: | The proposed approach achieves the best score (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records with contextual string embedds. |
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| Challenge: | Existing work on pharmacological entities requires manual annotation of these units. |
| Approach: | They propose an approach to task 1 of the PharmaCoNER Challenge to recognize pharmacological entities on a spanish corpus. |
| Outcome: | The proposed approach achieves 89.76% score on a spanish corpus based on pre-trained embeddings and 90.52% score on domain-specific embeddables. |
PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track (D19-57)
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Aitor Gonzalez-Agirre, Montserrat Marimon, Ander Intxaurrondo, Obdulia Rabal, Marta Villegas, Martin Krallinger
| Challenge: | Biomedical text mining is one of the most prolific application domains of natural language processing technologies. |
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VSP at PharmaCoNER 2019: Recognition of Pharmacological Substances, Compounds and Proteins with Recurrent Neural Networks in Spanish Clinical Cases (D19-57)
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| Challenge: | The Named Entity Recognition of drugs, medications and chemical entities in Spanish is a new task in the field of NLP . |
| Approach: | They propose to use SNOMED CT term search engine to classify the entities in Spanish and a neural model for the Named Entity Recognition. |
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Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text (D19-57)
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| Challenge: | Currently, the number of biomedical literature is growing at an exponential rate. |
| Approach: | They propose a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. |
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A Deep Learning-Based System for PharmaCoNER (D19-57)
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Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou
| Challenge: | Efficient access to mentions of clinical entities is very important for using clinical text. |
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NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection (D19-57)
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| Challenge: | Named entity recognition has been extensively studied on English news texts, but transfer to other domains and languages is still a challenging problem. |
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Using Snomed to recognize and index chemical and drug mentions. (D19-57)
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| Challenge: | a new named entity extraction system is proposed for biological texts . the system is based on machine learning and deep learning . |
| Approach: | They propose a named entity extraction system based on machine learning and deep learning . they propose to map drug names in Spanish biomedical texts using Snomed . |
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Biomedical Named Entity Recognition with Multilingual BERT (D19-57)
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| Challenge: | a multilingual model is not specifically tailored to either the language nor the application domain. |
| Approach: | They propose a CRF-based baseline approach and multilingual BERT to the task . they achieve an F-score of 88% on the development data and 87% on the test set with BERT . |
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INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning (2025.naacl-srw)
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| Challenge: | InsightBuddy-AI is a system for extracting medication mentions and their associated attributes. |
| Approach: | They propose a system for extracting medication mentions and their associated attributes . they use stacked and voting ensembles built upon pre-trained language models . |
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A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages (2024.lrec-main)
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Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, Pierre Zweigenbaum
| Challenge: | Existing clinical corpora mostly revolves around scientific articles in English . existing literature is limited to only a few scientific articles . |
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