ArticleJournal of biomedical informatics2013
Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
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- Attention-based interactive multi-level feature fusion for named entity recognition.Scientific reports · 2025Article
- Leveraging natural language processing to aggregate field safety notices of medical devices across the EU.NPJ digital medicine · 2024Article
- Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis.Journal of medical Internet research · 2024Article
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
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- A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection.Sensors (Basel, Switzerland) · 2023Article
- A multi-layer soft lattice based model for Chinese clinical named entity recognition.BMC medical informatics and decision making · 2022Article
- Making Biomedical Sciences publications more accessible for machines.Medicine, health care, and philosophy · 2022Article
- Composition-driven symptom phrase recognition for Chinese medical consultation corpora.BMC medical informatics and decision making · 2021Article
- Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation.International journal of environmental research and public health · 2021Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Feature extraction for phenotyping from semantic and knowledge resources.Journal of biomedical informatics · 2019Article
- Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods.JMIR medical informatics · 2018Article
- Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text.Journal of healthcare engineering · 2018Article
- Semantic annotation in biomedicine: the current landscape.Journal of biomedical semantics · 2017Review
Corrections and comments
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2 authors.
Funding
Abstract
Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work.
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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.