首页> 外文会议>Information Visualization '96, Proceedings IEEE Symposium on >DEPICT: Documents Evaluated as Pictures. Visualizing information using context vectors and self-organizing maps
【24h】

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

机译:描述:文档评估为图片。使用上下文向量和自组织映射可视化信息

获取原文

摘要

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.
机译:HNC Software,Inc.开发了一个名为DEPICT的系统,用于可视化大型文本语料库的信息内容。该系统围绕两种独立的神经网络方法构建:上下文向量和自组织映射。上下文向量(CV)是高维信息表示,对它们表示的文本实体的语义内容进行编码。自组织映射(SOM)能够将输入的高维信号空间转换为可用于可视化的低得多(通常为2或3个)维的输出空间。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用于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。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号