A. Mondal, I. Chaturvedi, Dipankar Das, Rajiv Bajpai, Sivaji Bandyopadhyay
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引用次数: 19
摘要
临床信息处理的不断复杂化促使像wordnet这样的医学事件词典的发展,以便向各自领域的专家(例如医疗从业者)和非专家(例如患者)传达有价值的信息(例如事件定义,基于感觉的上下文描述,极性等)。本文利用从SemEval 2015 Task-6中收集的医学事件种子列表、WordNet和英语医学词典三种不同的词汇资源,报道了临床文本中识别和描述事件、时间及其之间关系等医学术语的丰富。特别地,我们开发了用于医学事件的WordNet (WME),它使用上下文信息来消除医学术语的词义歧义,减少了医生和病人之间的沟通差距。我们提出了两种方法(顺序和组合),用于根据三种类型的文本中的每一种来确定医学事件的正确含义。利用极性词汇如SentiWordNet、Affect Word List和Taboda’s形容词List,实现了从词汇资源中提取医学事件的词汇表中基于极性的词义消歧。提出的WME在10-20%的范围内优于先前提出的Lesk词义消歧。
Lexical Resource for Medical Events: A Polarity Based Approach
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%.