{"title":"提高对动词的识别能力","authors":"Lynellen D. S. Perry","doi":"10.1145/2817460.2817470","DOIUrl":null,"url":null,"abstract":"In automatically assigning part-of-speech tags to scientific text, we find a high error rate when tagging main verbs. To reduce the occurrence of this serious error, we have created a neural network to search for main verbs that have been mis-tagged by a rule-based tagger. In this paper we describe our efforts to evolve the connection weights for the neural network, and to fractally configure another neural network for the same task.","PeriodicalId":274966,"journal":{"name":"ACM-SE 35","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improving the identification of verbs\",\"authors\":\"Lynellen D. S. Perry\",\"doi\":\"10.1145/2817460.2817470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In automatically assigning part-of-speech tags to scientific text, we find a high error rate when tagging main verbs. To reduce the occurrence of this serious error, we have created a neural network to search for main verbs that have been mis-tagged by a rule-based tagger. In this paper we describe our efforts to evolve the connection weights for the neural network, and to fractally configure another neural network for the same task.\",\"PeriodicalId\":274966,\"journal\":{\"name\":\"ACM-SE 35\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM-SE 35\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2817460.2817470\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM-SE 35","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2817460.2817470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In automatically assigning part-of-speech tags to scientific text, we find a high error rate when tagging main verbs. To reduce the occurrence of this serious error, we have created a neural network to search for main verbs that have been mis-tagged by a rule-based tagger. In this paper we describe our efforts to evolve the connection weights for the neural network, and to fractally configure another neural network for the same task.