OCTVis:基于本体的主题模型比较

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI:10.1109/VISUAL.2019.8933646

Amon Ge, Hyeju Jang, G. Carenini, K. Ho, Young ji Lee

{"title":"OCTVis:基于本体的主题模型比较","authors":"Amon Ge, Hyeju Jang, G. Carenini, K. Ho, Young ji Lee","doi":"10.1109/VISUAL.2019.8933646","DOIUrl":null,"url":null,"abstract":"Evaluating topic modeling results requires communication between domain and NLP experts. OCTVis is a visual interface to compare the quality of two topic models when mapped against a domain ontology. Its design is based on detailed data and task models, and was tested in a case study in the healthcare domain.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"OCTVis: Ontology-Based Comparison of Topic Models\",\"authors\":\"Amon Ge, Hyeju Jang, G. Carenini, K. Ho, Young ji Lee\",\"doi\":\"10.1109/VISUAL.2019.8933646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evaluating topic modeling results requires communication between domain and NLP experts. OCTVis is a visual interface to compare the quality of two topic models when mapped against a domain ontology. Its design is based on detailed data and task models, and was tested in a case study in the healthcare domain.\",\"PeriodicalId\":192801,\"journal\":{\"name\":\"2019 IEEE Visualization Conference (VIS)\",\"volume\":\"352 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Visualization Conference (VIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VISUAL.2019.8933646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Visualization Conference (VIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VISUAL.2019.8933646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

评估主题建模结果需要领域专家和NLP专家之间的沟通。OCTVis是一个可视化界面，用于在映射到领域本体时比较两个主题模型的质量。它的设计基于详细的数据和任务模型，并在医疗保健领域的案例研究中进行了测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

OCTVis: Ontology-Based Comparison of Topic Models

Evaluating topic modeling results requires communication between domain and NLP experts. OCTVis is a visual interface to compare the quality of two topic models when mapped against a domain ontology. Its design is based on detailed data and task models, and was tested in a case study in the healthcare domain.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 IEEE Visualization Conference (VIS)

自引率

0.00%

发文量