Named Entity Recognition in the Medical Domain with Constrained CRF Models
By Charles Jochim, Léa A. Deleris. 2017
12 citations recorded by the source corpus.
This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures “domain knowledge”, and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step.
Subfields: Artificial Intelligence, Molecular Biology
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Citations in the corpus
- Towards Open Intent Discovery for Conversational Text (2019, background): “Moreover, adding linguistic structure to existing models has been shown to improve their performance across a wide range of NLU tasks, such as word embedding [38], machine translation [4], named entity recognition [21], and semantic role labelling [16].”