{"title":"用联想线索组合叙述","authors":"Pierre-Luc Vaudry, G. Lapalme","doi":"10.18653/v1/W16-5501","DOIUrl":null,"url":null,"abstract":"A model is proposed showing how automatically extracted and manually written association rules can be used to build the structure of a narrative from real-life temporal data. The generated text’s communicative goal is to help the reader construct a causal representation of the events. A connecting associative thread allows the reader to follow associations from the beginning to the end of the text. It is created using a spanning tree over a selected associative sub-network. The results of a text quality evaluation show that the texts were understandable, but that flow between sentences, although not bad, could still be improved.","PeriodicalId":415027,"journal":{"name":"Workshop on Computational Creativity in Natural Language Generation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Assembling Narratives with Associative Threads\",\"authors\":\"Pierre-Luc Vaudry, G. Lapalme\",\"doi\":\"10.18653/v1/W16-5501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A model is proposed showing how automatically extracted and manually written association rules can be used to build the structure of a narrative from real-life temporal data. The generated text’s communicative goal is to help the reader construct a causal representation of the events. A connecting associative thread allows the reader to follow associations from the beginning to the end of the text. It is created using a spanning tree over a selected associative sub-network. The results of a text quality evaluation show that the texts were understandable, but that flow between sentences, although not bad, could still be improved.\",\"PeriodicalId\":415027,\"journal\":{\"name\":\"Workshop on Computational Creativity in Natural Language Generation\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Computational Creativity in Natural Language Generation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W16-5501\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Computational Creativity in Natural Language Generation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W16-5501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A model is proposed showing how automatically extracted and manually written association rules can be used to build the structure of a narrative from real-life temporal data. The generated text’s communicative goal is to help the reader construct a causal representation of the events. A connecting associative thread allows the reader to follow associations from the beginning to the end of the text. It is created using a spanning tree over a selected associative sub-network. The results of a text quality evaluation show that the texts were understandable, but that flow between sentences, although not bad, could still be improved.