Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
By Shaodian Zhang, Noémie Elhadad. 2013
245 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Steven Bethard
- Vikas Yadav
- André Freitas
- Mokanarangan Thayaparan
- Marco Valentino
- Mihai Surdeanu
- Christopher D. Manning
- Roi Reichart
In Molecular Biology
Citations in the corpus
- A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (2018, background): “Zhang and Elhadad (2013) used shallow syntactic knowledge and inverse document frequency (IDF) for an unsupervised NER system on biology (Kim et al., 2004) and medical (Uzuner et al., 2011) data, achieving 53.8% and 69.5% accuracy, respectively.”