{"title":"加权换能器的学习","authors":"Corinna Cortes, M. Mohri","doi":"10.3233/978-1-58603-975-2-14","DOIUrl":null,"url":null,"abstract":"Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.","PeriodicalId":286427,"journal":{"name":"Finite-State Methods and Natural Language Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Learning with Weighted Transducers\",\"authors\":\"Corinna Cortes, M. Mohri\",\"doi\":\"10.3233/978-1-58603-975-2-14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.\",\"PeriodicalId\":286427,\"journal\":{\"name\":\"Finite-State Methods and Natural Language Processing\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Finite-State Methods and Natural Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/978-1-58603-975-2-14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Finite-State Methods and Natural Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/978-1-58603-975-2-14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.
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