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A light knowledge model for linguistic applications.

机译:语言应用的轻知识模型。

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摘要

Content extraction from medical texts is achievable today by linguistic applications, in so far as sufficient domain knowledge is available. Such knowledge represents a model of the domain and is hard to collect with sufficient depth and good coverage, despite numerous attempts. To leverage this task is a priority in order to benefit from the awaited linguistic tools. The light model is designed with this goal in mind. Syntactic and lexical information are generally available with large lexicons. A domain model should add the necessary semantic information. The authors have designed a light knowledge model for the collection of semantic information on the basis of the recognized syntactical and lexical attributes. It has been tailored for the acquisition of enough semantic information in order to retrieve terms of a controlled vocabulary from free texts, as for example, to retrieve Mesh terms from patient records.
机译:目前,只要有足够的领域知识,就可以通过语言应用程序从医学文本中提取内容。尽管进行了许多尝试,但是这种知识代表了领域的模型,并且很难以足够的深度和良好的覆盖范围进行收集。为了从等待已久的语言工具中受益,优先考虑利用此任务。设计灯光模型时要牢记这一目标。大型词典通常提供语法和词汇信息。域模型应添加必要的语义信息。作者基于公认的句法和词汇属性,设计了一个用于收集语义信息的轻型知识模型。它已针对获取足够的语义信息进行了量身定制,以便从自由文本中检索受控词汇的术语,例如,从患者记录中检索Mesh术语。

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