{"title":"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","authors":"David A. Rushall, M. Ilgen","doi":"10.1109/INFVIS.1996.559228","DOIUrl":null,"url":null,"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.","PeriodicalId":153504,"journal":{"name":"Proceedings IEEE Symposium on Information Visualization '96","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"DEPICT: Documents Evaluated as Pictures. Visualizing information using context vectors and self-organizing maps\",\"authors\":\"David A. Rushall, M. Ilgen\",\"doi\":\"10.1109/INFVIS.1996.559228\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":153504,\"journal\":{\"name\":\"Proceedings IEEE Symposium on Information Visualization '96\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE Symposium on Information Visualization '96\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFVIS.1996.559228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE Symposium on Information Visualization '96","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFVIS.1996.559228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DEPICT: Documents Evaluated as Pictures. Visualizing information using context vectors and self-organizing maps
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.