{"title":"基于语义框架注解的需求文档语料库","authors":"Waad Alhoshan, R. Batista-Navarro, Liping Zhao","doi":"10.1109/RE.2018.00055","DOIUrl":null,"url":null,"abstract":"Software requirements are typically written in natural language, which need to be transformed into a more formal representation. Natural language processing techniques have been applied to aid in this transformation. Semantic parsing, for instance, adds semantic structure to text. It however requires supporting corpora which are still missing in requirements engineering. To address this gap, we developed FN-RE, a corpus of requirements documents, which was annotated based on semantic frames in FrameNet. Each requirement statement was manually labelled by two annotators by selecting suitable semantic frames and related frame elements. We obtained an average agreement of 72.85% between the two annotators, measured by F-score, thus indicating that the annotations provided in our corpus are reliable.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Towards a Corpus of Requirements Documents Enriched with Semantic Frame Annotations\",\"authors\":\"Waad Alhoshan, R. Batista-Navarro, Liping Zhao\",\"doi\":\"10.1109/RE.2018.00055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software requirements are typically written in natural language, which need to be transformed into a more formal representation. Natural language processing techniques have been applied to aid in this transformation. Semantic parsing, for instance, adds semantic structure to text. It however requires supporting corpora which are still missing in requirements engineering. To address this gap, we developed FN-RE, a corpus of requirements documents, which was annotated based on semantic frames in FrameNet. Each requirement statement was manually labelled by two annotators by selecting suitable semantic frames and related frame elements. We obtained an average agreement of 72.85% between the two annotators, measured by F-score, thus indicating that the annotations provided in our corpus are reliable.\",\"PeriodicalId\":445032,\"journal\":{\"name\":\"2018 IEEE 26th International Requirements Engineering Conference (RE)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 26th International Requirements Engineering Conference (RE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RE.2018.00055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 26th International Requirements Engineering Conference (RE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RE.2018.00055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a Corpus of Requirements Documents Enriched with Semantic Frame Annotations
Software requirements are typically written in natural language, which need to be transformed into a more formal representation. Natural language processing techniques have been applied to aid in this transformation. Semantic parsing, for instance, adds semantic structure to text. It however requires supporting corpora which are still missing in requirements engineering. To address this gap, we developed FN-RE, a corpus of requirements documents, which was annotated based on semantic frames in FrameNet. Each requirement statement was manually labelled by two annotators by selecting suitable semantic frames and related frame elements. We obtained an average agreement of 72.85% between the two annotators, measured by F-score, thus indicating that the annotations provided in our corpus are reliable.