DEPICT: Documents Evaluated as Pictures. Visualizing information using context vectors and self-organizing maps

David A. Rushall, M. Ilgen
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引用次数: 22

Abstract

HNC Software, Inc. has developed a system called DEPICT for visualizing the information content of large textual corpora. The system is built around two separate neural network methodologies: context vectors and self-organizing maps. Context vectors (CVs) are high dimensional information representations that encode the semantic content of the textual entities they represent. Self-organizing maps (SOMs) are capable of transforming an input, high dimensional signal space into a much lower (usually two or three) dimensional output space useful for visualization. Neither process requires human intervention, nor an external knowledge base. Together, these neural network techniques can be utilized to automatically identify the relevant information themes present in a corpus, and present those themes to the user in a intuitive visual form.
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描述:作为图片评估的文件。使用上下文向量和自组织地图可视化信息
HNC软件公司开发了一个名为“描述”的系统,用于可视化大型文本语料库的信息内容。该系统是围绕两种独立的神经网络方法构建的:上下文向量和自组织地图。上下文向量(CVs)是一种高维的信息表示,它对所表示的文本实体的语义内容进行编码。自组织映射(SOMs)能够将输入的高维信号空间转换为对可视化有用的低得多的(通常是二维或三维)输出空间。流程既不需要人工干预,也不需要外部知识库。总之,这些神经网络技术可以用来自动识别语料库中存在的相关信息主题,并以直观的视觉形式将这些主题呈现给用户。
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