{"title":"与平板电脑阅读入门互动的建模讨论主题","authors":"Adrian Boteanu, S. Chernova","doi":"10.1145/2449396.2449409","DOIUrl":null,"url":null,"abstract":"CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills and shared parent-child reading through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and leverages this information, in combination with a common sense knowledge base, to develop discussion topic models. The long-term goal of the project is to use such models to provide context-sensitive discussion topic suggestions to parents during the shared reading activity in order to enhance the interactive experience and foster parental engagement in literacy education. In this paper, we present a novel approach for using commonsense reasoning to effectively model topics of discussion in unstructured dialog. We introduce a metric for localizing concepts that the users are interested in at a given moment in the dialog and extract a time sequence of words of interest. We then present algorithms for topic modeling and refinement that leverage semantic knowledge acquired from ConceptNet, a commonsense knowledge base. We evaluate the performance of our algorithms using transcriptions of audio recordings of parent-child pairs interacting with a tablet application, and compare the output of our algorithms to human-generated topics. Our results show that words of interest and discussion topics selected by our algorithm closely match those identified by human readers.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Modeling discussion topics in interactions with a tablet reading primer\",\"authors\":\"Adrian Boteanu, S. Chernova\",\"doi\":\"10.1145/2449396.2449409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills and shared parent-child reading through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and leverages this information, in combination with a common sense knowledge base, to develop discussion topic models. The long-term goal of the project is to use such models to provide context-sensitive discussion topic suggestions to parents during the shared reading activity in order to enhance the interactive experience and foster parental engagement in literacy education. In this paper, we present a novel approach for using commonsense reasoning to effectively model topics of discussion in unstructured dialog. We introduce a metric for localizing concepts that the users are interested in at a given moment in the dialog and extract a time sequence of words of interest. We then present algorithms for topic modeling and refinement that leverage semantic knowledge acquired from ConceptNet, a commonsense knowledge base. We evaluate the performance of our algorithms using transcriptions of audio recordings of parent-child pairs interacting with a tablet application, and compare the output of our algorithms to human-generated topics. Our results show that words of interest and discussion topics selected by our algorithm closely match those identified by human readers.\",\"PeriodicalId\":87287,\"journal\":{\"name\":\"IUI. International Conference on Intelligent User Interfaces\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUI. International Conference on Intelligent User Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2449396.2449409\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI. International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2449396.2449409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling discussion topics in interactions with a tablet reading primer
CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills and shared parent-child reading through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and leverages this information, in combination with a common sense knowledge base, to develop discussion topic models. The long-term goal of the project is to use such models to provide context-sensitive discussion topic suggestions to parents during the shared reading activity in order to enhance the interactive experience and foster parental engagement in literacy education. In this paper, we present a novel approach for using commonsense reasoning to effectively model topics of discussion in unstructured dialog. We introduce a metric for localizing concepts that the users are interested in at a given moment in the dialog and extract a time sequence of words of interest. We then present algorithms for topic modeling and refinement that leverage semantic knowledge acquired from ConceptNet, a commonsense knowledge base. We evaluate the performance of our algorithms using transcriptions of audio recordings of parent-child pairs interacting with a tablet application, and compare the output of our algorithms to human-generated topics. Our results show that words of interest and discussion topics selected by our algorithm closely match those identified by human readers.