| Challenge: | Named entity recognition (NER) and entity normalization (entity linking) are two fundamental natural language processing tasks to achieve entity normalizing. |
| Approach: | They propose a CNN method that normalizes microbiology-related entities to concepts in standard dictionaries. |
| Outcome: | The proposed method performs well in the BioNLP-OST19 shared task Bacteria Biotope. |
Similar Papers
Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information (2020.lrec-1)
Copied to clipboard
Arnaud Ferré, Robert Bossy, Mouhamadou Ba, Louise Deléger, Thomas Lavergne, Pierre Zweigenbaum, Claire Nédellec
| Challenge: | Entity normalization is an important subtask of information extraction . it links entities mentions in text to categories or concepts in a reference vocabulary . |
| Approach: | They propose a method that uses corpus selection, pre-processing and weak supervision strategies to address the scarcity of training data. |
| Outcome: | The proposed method outperforms state-of-the-art methods in terms of accuracy and parametrization . it uses corpus selection, pre-processing and weak supervision strategies . |
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes (D19-57)
Copied to clipboard
| Challenge: | In 2011, the Bacteria Biotope Task was conducted for the first time as a part of the BioNLP Shared Task targeting the extraction of useful information regarding bacteria and their habitats. |
| Approach: | They propose two systems for the normalization of entities and the identification of relations between entities given a biomedical text. |
| Outcome: | The proposed method performs as good as deep learning based methods which require labeled data. |
BioEL: A Comprehensive Python Package for Biomedical Entity Linking (2025.findings-naacl)
Copied to clipboard
Prasanth Bathala, Christophe Ye, Batuhan Nursal, Shubham Lohiya, David Kartchner, Cassie S. Mitchell
| Challenge: | Entity Linking in biomedical literature is a critical task that enhances the extraction and integration of information from diverse scientific literature. |
| Approach: | They propose a Python package that allows for better Entity Linking in biomedical literature . the package includes four components: Ontology Object, Dataset Object and Evaluation Framework . |
| Outcome: | The proposed open-source package enables the implementation and comparison of biomedical entity linking tasks. |
Clustering-based Inference for Biomedical Entity Linking (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to linking entities ignore relationships between entities in biomedical knowledge bases. |
| Approach: | They propose a model which can link mentions of unseen entities using learned representations of entities. |
| Outcome: | The proposed model improves on the largest publicly available biomedical dataset by 3.0 points of accuracy and 2.3 points of reliability. |
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (D19-57)
Copied to clipboard
| Challenge: | BB system is among the top two systems in five of all six subtasks . knowledge about microbial diversity is crucial for the study of microbiome and bacteria . |
| Approach: | They present a system that uses word embedding and lexical features to perform entities recognition, normalization and relation extraction. |
| Outcome: | The proposed system achieves state-of-the-art in five of six subtasks and is among the top two in five. |
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing attempts to apply large language models to BioEL have revealed difficulties . |
| Approach: | They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities . |
| Outcome: | Extensive experiments show that the framework outperforms existing LLMs. |
Deep Neural Models for Medical Concept Normalization in User-Generated Texts (P19-2)
Copied to clipboard
| Challenge: | a medical concept normalization problem is a challenge since social media texts are ambiguous and noisy . a recent study shows that neural architectures leverage the semantic meaning of the entity mention . |
| Approach: | They propose to map a health-related entity mention to a controlled vocabulary . they use powerful neural networks and contextualized word representation models . |
| Outcome: | The proposed model outperforms existing state-of-the-art models in mapping medical concepts to medical terms . the proposed model is based on recurrent neural networks and contextualized word representation models . |
LLM as Entity Disambiguator for Biomedical Entity-Linking (2025.acl-short)
Copied to clipboard
| Challenge: | Entity linking involves normalizing a mention in medical text to a unique identifier in a knowledge base, such as UMLS or MeSH. |
| Approach: | They propose to use a large language model as an entity disambiguator to enhance the accuracy of alias-matching entity linking methods. |
| Outcome: | The proposed method surpasses existing methods on biomedical datasets by up to 16 points in accuracy. |
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
Copied to clipboard
| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
Improving Fine-grained Entity Typing with Entity Linking (D19-1)
Copied to clipboard
| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
| Approach: | They propose a deep neural model that uses context and information from entity linking to improve fine-grained entity typing. |
| Outcome: | The proposed model achieves 5% absolute strict accuracy improvement over the state of the art on two datasets. |