{"title":"基于神经网络的周期消歧","authors":"T. Humphrey, Fu-qiu Zhou","doi":"10.1109/IJCNN.1989.118427","DOIUrl":null,"url":null,"abstract":"Summary form only given. A problem that has never been addressed in the literature is the problem of machine recognition of sentences in real-world documents (i.e. identifying the beginning and end of a sentence). A description is given of the problem and experiments that show that a feedforward neural network can be trained, using the backpropagation learning algorithm, to disambiguate periods. The authors also present results that indicate that a low training tolerance improves a neural network's ability to generalize at the cost of a dramatic increase in the number of training iterations.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Period disambiguation using a neural network\",\"authors\":\"T. Humphrey, Fu-qiu Zhou\",\"doi\":\"10.1109/IJCNN.1989.118427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. A problem that has never been addressed in the literature is the problem of machine recognition of sentences in real-world documents (i.e. identifying the beginning and end of a sentence). A description is given of the problem and experiments that show that a feedforward neural network can be trained, using the backpropagation learning algorithm, to disambiguate periods. The authors also present results that indicate that a low training tolerance improves a neural network's ability to generalize at the cost of a dramatic increase in the number of training iterations.<<ETX>>\",\"PeriodicalId\":199877,\"journal\":{\"name\":\"International 1989 Joint Conference on Neural Networks\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International 1989 Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1989.118427\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International 1989 Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1989.118427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Summary form only given. A problem that has never been addressed in the literature is the problem of machine recognition of sentences in real-world documents (i.e. identifying the beginning and end of a sentence). A description is given of the problem and experiments that show that a feedforward neural network can be trained, using the backpropagation learning algorithm, to disambiguate periods. The authors also present results that indicate that a low training tolerance improves a neural network's ability to generalize at the cost of a dramatic increase in the number of training iterations.<>