| 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 . |
| Outcome: | The proposed system achieves 78% in the first sub-track and 72% in the second task. |
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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. |
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. |
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. |
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. |
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. |
A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (2022.lrec-1)
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| Challenge: | Biomedical researchers have used manual curation to extract biomedical interactions from research texts to improve coverage. |
| Approach: | They propose a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models using human annotations and the CTD database. |
| Outcome: | The proposed dataset is substantially larger and cleaner than existing datasets and includes annotations linking mentions to their entities. |
Method Entity Extraction from Biomedical Texts (2022.coling-1)
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| Challenge: | Scientific research papers consist of complex keywords and domain-specific terminologies, and new terminologie erupt. |
| Approach: | They find method terminologies in biomedical text using rule-based and machine learning techniques . authors propose to use a silver standard corpus to extract method entities from biomedically text . |
| Outcome: | The proposed method entities can be extracted from biomedical text with reasonable accuracy . the proposed method entity extraction method is based on a rule-based method and a machine learning technique. |
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. |
| 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. |
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. |
| Approach: | They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages. |
| Outcome: | The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text. |