WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts Anupam Mondal1 Dipankar Das1 Erik Cambria2 Sivaji Bandyopadhyay1 1Department of Computer Science and Engineering 2School of Computer Science and Engineering Jadavpur University, Kolkata, India Nanyang Technological University, Singapore
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[email protected] Abstract However, medical text is in general unstructured since doctors do not like to fill forms and pre- Information extraction in the medical do- fer free-form notes of their observations. Hence, main is laborious and time-consuming due a lexical design is difficult due to lack of any to the insufficient number of domain- prior knowledge of medical terms and contexts. specific lexicons and lack of involve- Therefore, we are motivated to enhance a med- ment of domain experts such as doctors ical lexicon namely WordNet of Medical Events and medical practitioners. Thus, in the (WME 2.0) which helps to identify medical con- present work, we are motivated to de- cepts and their features. In order to enrich this sign a new lexicon, WME 3.0 (WordNet lexicon, we have employed various well-known of Medical Events), which contains over resources like conventional WordNet, SentiWord- 10,000 medical concepts along with their Net (Esuli and Sebastiani, 2006), SenticNet (Cam- part of speech, gloss (descriptive expla- bria et al., 2016), Bing Liu (Liu, 2012), and nations), polarity score, sentiment, sim- Taboada’s Adjective list (Taboada et al., 2011) ilar sentiment words, category, affinity and a preprocessed English medical dictionary1 on score and gravity score features.