Automatic labelling of important terms and phrases from medical discussions

Ranjan, Harsh; Agarwal, Sumit; Prakash, Amit; Saha, Sujan Kumar

doi:10.1109/infocomtech.2017.8340644

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Unsupervised methods can be applied to large-scale scenarios [26, 27]. Alternatively, a hybrid strategy may be used based on a set of statistical measures and syntactical and semantic information [28]. “Off the shelf” type of IR algorithms can be utilized quite successfully in some of the scenarios, especially with limited focus areas (in a medical sense) [29].…”

Section: Related Workmentioning

confidence: 99%

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records

Žitkus

Butkienė

Butleris

et al. 2019

Computational and Mathematical Methods in Medicine

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Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

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Section: Related Workmentioning

confidence: 99%