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. |
| Approach: | They developed a pipeline system based on deep learning methods for this shared task . it achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average LSTM score of 0.8391 on track 2 . |
| Outcome: | The proposed system achieves a micro-average F1-score of 0.9105 on track 1 and a mini-average score of 0.8391 on track 2. |
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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. |
| Approach: | They propose to share a task on detecting drug and chemical entities in medical documents in Spanish with other languages to improve access to biomedical text mining. |
| Outcome: | The first task on detecting drug and chemical entities in Spanish medical documents yielded competitive results with F-measures above 0.91. |
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models (D19-57)
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| Challenge: | NER and concept indexing perform named entity recognition and concept identifiers (CUIs) in a knowledge base. |
| Approach: | They propose a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI) they use bi-LSTM to capture the semantic information of a sequence and classify them into entities or no entities . |
| Outcome: | The proposed approach performs named entity recognition (NER) and concept indexing (CI) which links them to concept unique identifiers (CUIs) in a knowledge base. |
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task (D19-57)
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| Challenge: | Existing methods for natural language processing are labor-intensive and skill-dependent . Currently, most biomedical natural language tasks focus on English documents . |
| Approach: | They introduce a BERT benchmark to facilitate the research of PharmaCoNER task . they evaluate two baselines based on Multilingual BERT and BioBERT on the corpus . |
| Outcome: | The proposed task is based on multilingual BERT and BioBERT on the PharmaCoNER corpus. |
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. |
| Outcome: | The proposed system achieves 76.29% and 60.34% performance in the Named Entity Recognition and Concept indexing tasks. |
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. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the PharmaCoNER corpus . the proposed model is based on two bidirectional long-term memory and conditional random field networks . |
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish (D19-57)
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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. |
IxaMed at PharmacoNER Challenge 2019 (D19-57)
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| 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. |
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 . |
| Outcome: | The proposed system outperforms fine-tuned models in the extraction of medication mentions and associated attributes. |
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. |
| Approach: | They propose a system that provides a non-standard domain and language setting for pharmacological entity detection in Spanish texts and a sequencelabeling task that requires neither language nor domain expertise. |
| Outcome: | The proposed system achieves up to 88.6% F1 in the PharmaCoNER competition and is based on a sequence labeling task and training on annotated data. |
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 . |
| Outcome: | The proposed model achieves an F score of 88% on the development data and 87% on the test set with BERT. |