ArticleBioinformatics (Oxford, England)2017
Deep learning with word embeddings improves biomedical named entity recognition.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.
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Who cites it
117 citing papers in PubMed.
- The Role of N-ary Relations in Representing Biomedical Complexity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- TimeX: Phenotype Onset Extraction from Clinical Narratives.npj health systems · 2026Article
- iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.Interdisciplinary sciences, computational life sciences · 2025Article
- Automated Data Harmonization in Clinical Research: Natural Language Processing Approach.JMIR formative research · 2025Article
- Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion.Scientific reports · 2025Article
- Chinese medical named entity recognition integrating adversarial training and feature enhancement.Scientific reports · 2025Article
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
- AgriBioNER: A Named Entity Recognition Tool for Identification of ncRNA and Diseases in Agricultural Literature.Current genomics · 2025Article
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.3D printing and additive manufacturing · 2024Article
- Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.Database : the journal of biological databases and curation · 2024Article
- Towards discovery: an end-to-end system for uncovering novel biomedical relations.Database : the journal of biological databases and curation · 2024Article
- Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.Journal of proteome research · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- GPDminer: a tool for extracting named entities and analyzing relations in biological literature.BMC bioinformatics · 2024Article
- Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
57 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
motivationText mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult.
resultsWe show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall. AVAILABILITY AND IMPLEMENTATION: The source code for LSTM-CRF is available at https://github.com/glample/tagger and the links to the corpora are available at https://corposaurus.github.io/corpora/ . CONTACT: habibima@informatik.hu-berlin.de.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.