{"title":"Automatic NLP-based enrichment of E-learning content for English language learning","authors":"Andreas Schulz, J. Lassig","doi":"10.1109/ICETA.2014.7107623","DOIUrl":null,"url":null,"abstract":"The creation of quality content for E-learning resources is a time-consuming task. To simplify the process of content creation for language learning and enable easy adaptability for different requirements and language levels we strive to add as much automation as possible. In order to still obtain high quality, we present in this paper our approaches to enrich E-learning-based English vocabulary tests, which support blended learning and improve direct user feedback. We integrate openly available language resources for selecting and appending usage example sentences for a given vocabulary corpus. Furthermore we discuss our results and suggest to acquire natural language processing (NLP) based techniques to improve the generation of language related contents in general and to overcome some of the weaknesses of our current solution.","PeriodicalId":340996,"journal":{"name":"2014 IEEE 12th IEEE International Conference on Emerging eLearning Technologies and Applications (ICETA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 12th IEEE International Conference on Emerging eLearning Technologies and Applications (ICETA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETA.2014.7107623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The creation of quality content for E-learning resources is a time-consuming task. To simplify the process of content creation for language learning and enable easy adaptability for different requirements and language levels we strive to add as much automation as possible. In order to still obtain high quality, we present in this paper our approaches to enrich E-learning-based English vocabulary tests, which support blended learning and improve direct user feedback. We integrate openly available language resources for selecting and appending usage example sentences for a given vocabulary corpus. Furthermore we discuss our results and suggest to acquire natural language processing (NLP) based techniques to improve the generation of language related contents in general and to overcome some of the weaknesses of our current solution.