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Lexical Resource for Medical Events: A Polarity Based Approach

机译:医疗事件的词汇资源:一种基于极性的方法

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The continuous sophistication in clinical informationprocessing motivates the development of a dictionary likeWordNet for Medical Events in order to convey the valuableinformation (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%.
机译:临床信息处理中的不断完善促使诸如WordNet的医学事件词典的发展,以便向专家(例如,医疗从业人员)和非专家(例如,患者)传达有价值的信息(例如,事件定义,基于感觉的语境描述,极性等)。 )在各自的字段中。本文通过利用三种不同的词汇资源(即从SemEval 2015 Task-6,WordNet和英语中收集的医疗事件的种子列表)报告了医学术语的丰富性,例如在临床文本中识别和描述事件,时间以及它们之间的关系。医学词典。特别是,我们开发了用于医疗事件的WordNet(WME),该软件使用上下文信息来消除医学术语的词义歧义,并缩小了医患之间的沟通差距。我们基于三种文本中的每一种,提出了两种方法(顺序方法和组合方法)来识别医疗事件的正确含义。诸如SentiWordNet,Affect Word List和Taboda的形容词列表之类的极性词典已被用于实现从医学词汇中提取词汇的基于极性的医学事件的词义消歧,这些词汇是从词汇资源中提取的。拟议的WME胜过先前提出的Lesk Word Sense歧义消除,范围在10%至20%之间。

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