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Neurolinguistic Approach to Natural Language Processing with Applications to Medical Text Analysis

机译：神经语言处理自然语言的方法及其在医学文本分析中的应用

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Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts that are not found directly in the text. Approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications.
机译：理解书面或口头语言大概涉及在大脑中传播神经激活。可以通过在语义网络中扩展激活来提供近似表示，该增强表示涉及涉及直接在文本中找不到的概念的表示，可以近似该过程。该过程的近似具有很大的实践和理论意义。尽管涉及单词表示的神经回路的激活在时间上迅速变化，但是可以在矢量模型中捕获通过关联网络传播的这些激活的快照。相似类型的概念会激活较大的神经元簇，并在左右半球启动区域。对最近的大脑成像实验的分析显示了右半球非语言聚类的重要性。医学本体论使得能够开发大规模的实用算法来重新创建传播神经激活的途径。首先在文本中标识特定语义类型的概念，然后将相同类型的所有相关概念添加到文本中，以提供扩展的表示形式。为了避免在每个步骤之后扩展功能空间的快速增长，仅保留增加文档聚类的最有用功能。简短的出院摘要用于说明此过程如何根据真实的，非常嘈杂的数据进行工作。展开的文本显示出明显改善的聚类，并且可以以更高的准确性进行分类。尽管可以设计出更好的近似神经激活的分布，但本文提出的一种实用方法有助于发现大脑用来处理特定概念的途径，并且可以用于大规模应用。

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