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Concepts extraction for medical documents using ontology

机译:使用本体提取医学文档的概念

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摘要

In the biomedical domain large amount of text documents are unstructured information is available in digital text form. Text Mining is the method or technique to find for interesting and useful information from unstructured text. Text Mining is also an important task in medical domain. The technique uses for Information retrieval, Information extraction and natural language processing (NLP). Traditional approaches for information retrieval are based on key based similarity. These approaches are used to overcome these problems; Semantic text mining is to discover the hidden information from unstructured text and making relationships of the terms occurring in them. In the biomedical text, the text should be in the form of text which can be present in the books, articles, literature abstracts, and so forth. Most of information is stored in the text format, so in this paper we will focus on the role of ontology for semantic text mining by using WordNet. Specifically, we have presented a model for extracting concepts from text documents using linguistic ontology in the domain of medical.
机译:在生物医学领域,大量文本文档是非结构化信息,可以数字文本形式获得。文本挖掘是从非结构化文本中查找有趣和有用信息的方法或技术。文本挖掘也是医学领域的重要任务。该技术用于信息检索,信息提取和自然语言处理(NLP)。信息检索的传统方法基于基于密钥的相似性。这些方法用于克服这些问题。语义文本挖掘是要从非结构化文本中发现隐藏的信息,并使其中出现的术语相互关联。在生物医学文本中,文本应采用可以在书籍,文章,文献摘要等中出现的文本形式。大多数信息以文本格式存储,因此在本文中,我们将重点介绍本体在使用WordNet进行语义文本挖掘中的作用。具体而言,我们提出了一种在医学领域中使用语言本体从文本文档中提取概念的模型。

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