Lexical resource for medical events: a polarity based approach

Mondal, Anupam, Chaturvedi, Iti, Das, Dipankar, Bajpai, Rajiv, and Bandyopadhyay, Sivaji (2016) Lexical resource for medical events: a polarity based approach. In: Proceedings of the IEEE International Conference on Data Mining Workshops. pp. 1302-1309. From: ICDMW 2015: 15th IEEE International Conference on Data Mining Workshops, 14-17 November 2015, Atlantic City, NY, USA.

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The continuous sophistication in clinical information processing motivates the development of a dictionary like WordNet for Medical Events in order to convey the valuable information (e.g., event definition, sense based contextualdescription, polarity etc.) to the experts (e.g. medicalpractitioners) and non-experts (e.g. patients) in their respective fields. The present paper reports the enrichment of medical terms such as identifying and describing events, times and the relations between them in clinical text by employing three different lexical resources namely seed list of medical events collected from SemEval 2015 Task-6, the WordNet and an English medical dictionary. In particular, we develop WordNet for Medical Events (WME) that uses contextual information for word sense disambiguation of medical terms and reduce the communication gap between doctors and patients. We have proposed two approaches (Sequential and Combined) for identifying the proper sense of a medical event based on each of the three types of texts. The polarity lexicons e.g., SentiWordNet, Affect Word List and Taboda's adjective list have been used for implementing the polarity based Word Sense Disambiguation of the medical events from their glosses as extracted from the lexicalresources. The proposed WME out-performed a previouslyproposed Lesk Word Sense Disambiguation in the range of 10-20%.

Item ID: 63355
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4673-8493-3
Keywords: Medical Event, Polarity, Word Sense Disambiguation, WordNet
Copyright Information: © 2015 IEEE.
Date Deposited: 08 Jul 2020 23:11
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 70%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3208 Medical physiology > 320899 Medical physiology not elsewhere classified @ 30%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920401 Behaviour and Health @ 100%
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