{"title":"从例子中生成双关语谜语","authors":"B. Hong, Ethel Ong","doi":"10.1109/ISUC.2008.28","DOIUrl":null,"url":null,"abstract":"Text generation systems, such as pun generators, depend on manually created templates which require a lot of effort to build. This paper presents T-Peg, a system that utilizes semantic and phonetic knowledge sources to automatically capture the wordplay patterns of human-made jokes from training examples. The knowledge learned are stored as templates which, combined with a keyword input from the user, can then be used to generate punning riddles. Manual evaluation by a linguist on the completeness of the learned templates gave the system a score of 4.0 out of 5. User feedback gave the computer-generated puns an average score of 2.13 out of 5, as compared to their human-made counterparts which received an average score of 2.70, demonstrating that computers can be trained to be as humorous as humans.","PeriodicalId":339811,"journal":{"name":"2008 Second International Symposium on Universal Communication","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Generating Punning Riddles from Examples\",\"authors\":\"B. Hong, Ethel Ong\",\"doi\":\"10.1109/ISUC.2008.28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Text generation systems, such as pun generators, depend on manually created templates which require a lot of effort to build. This paper presents T-Peg, a system that utilizes semantic and phonetic knowledge sources to automatically capture the wordplay patterns of human-made jokes from training examples. The knowledge learned are stored as templates which, combined with a keyword input from the user, can then be used to generate punning riddles. Manual evaluation by a linguist on the completeness of the learned templates gave the system a score of 4.0 out of 5. User feedback gave the computer-generated puns an average score of 2.13 out of 5, as compared to their human-made counterparts which received an average score of 2.70, demonstrating that computers can be trained to be as humorous as humans.\",\"PeriodicalId\":339811,\"journal\":{\"name\":\"2008 Second International Symposium on Universal Communication\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Second International Symposium on Universal Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISUC.2008.28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Second International Symposium on Universal Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISUC.2008.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Text generation systems, such as pun generators, depend on manually created templates which require a lot of effort to build. This paper presents T-Peg, a system that utilizes semantic and phonetic knowledge sources to automatically capture the wordplay patterns of human-made jokes from training examples. The knowledge learned are stored as templates which, combined with a keyword input from the user, can then be used to generate punning riddles. Manual evaluation by a linguist on the completeness of the learned templates gave the system a score of 4.0 out of 5. User feedback gave the computer-generated puns an average score of 2.13 out of 5, as compared to their human-made counterparts which received an average score of 2.70, demonstrating that computers can be trained to be as humorous as humans.