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Finite-State Methods and Natural Language Processing最新文献

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Characterizations of Regularity 正则性的特征
Finite-State Methods and Natural Language Processing Pub Date : 2005-09-01 DOI: 10.1007/11780885_1
T. Harju
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引用次数: 9
Using Finite State Technology in a Tool for Linguistic Exploration 有限状态技术在语言探索工具中的应用
Finite-State Methods and Natural Language Processing Pub Date : 2005-09-01 DOI: 10.1007/11780885_19
Kemal Oflazer, M. D. Erbas, M. Erdogmus
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引用次数: 0
Partitioning Multitape Transducers 多磁带传感器分区
Finite-State Methods and Natural Language Processing Pub Date : 2005-09-01 DOI: 10.1007/11780885_3
François Barthélemy
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引用次数: 5
Klex: A Finite-State Transducer Lexicon of Korean 韩文有限状态换能器词典
Finite-State Methods and Natural Language Processing Pub Date : 2005-09-01 DOI: 10.1007/11780885_8
Na-Rae Han
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引用次数: 3
FIRE Station 消防局
Finite-State Methods and Natural Language Processing Pub Date : 1900-01-01 DOI: 10.1007/11780885_39
B. Watson
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引用次数: 3
A Memory-efficient epsilon-Removal Algorithm for Weighted Acyclic Finite-State Automata 加权无循环有限状态自动机的一种内存高效去除算法
Finite-State Methods and Natural Language Processing Pub Date : 1900-01-01 DOI: 10.3233/978-1-58603-975-2-72
T. Hanneforth
{"title":"A Memory-efficient epsilon-Removal Algorithm for Weighted Acyclic Finite-State Automata","authors":"T. Hanneforth","doi":"10.3233/978-1-58603-975-2-72","DOIUrl":"https://doi.org/10.3233/978-1-58603-975-2-72","url":null,"abstract":"","PeriodicalId":286427,"journal":{"name":"Finite-State Methods and Natural Language Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130918642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Finite-State Methods and Natural Language Processing, 8th International Workshop, FSMNLP 2009, Pretoria, South Africa, July 21-24, 2009, Revised Selected Papers 有限状态方法与自然语言处理，第八届国际研讨会，FSMNLP 2009，比勒陀利亚，南非，2009年7月21-24日，论文选集
Finite-State Methods and Natural Language Processing Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-14684-8
Anssi Yli-Jyrä, András Kornai, J. Sakarovitch, B. Watson
{"title":"Finite-State Methods and Natural Language Processing, 8th International Workshop, FSMNLP 2009, Pretoria, South Africa, July 21-24, 2009, Revised Selected Papers","authors":"Anssi Yli-Jyrä, András Kornai, J. Sakarovitch, B. Watson","doi":"10.1007/978-3-642-14684-8","DOIUrl":"https://doi.org/10.1007/978-3-642-14684-8","url":null,"abstract":"","PeriodicalId":286427,"journal":{"name":"Finite-State Methods and Natural Language Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Silent HMMs: Generalized Representation of Hidden Semi-Markov Models and Hierarchical HMMs 隐式半马尔可夫模型和层次hmm的广义表示
Finite-State Methods and Natural Language Processing Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3113
Kei Wakabayashi
{"title":"Silent HMMs: Generalized Representation of Hidden Semi-Markov Models and Hierarchical HMMs","authors":"Kei Wakabayashi","doi":"10.18653/v1/W19-3113","DOIUrl":"https://doi.org/10.18653/v1/W19-3113","url":null,"abstract":"Modeling sequence data using probabilistic finite state machines (PFSMs) is a technique that analyzes the underlying dynamics in sequences of symbols. Hidden semi-Markov models (HSMMs) and hierarchical hidden Markov models (HHMMs) are PFSMs that have been successfully applied to a wide variety of applications by extending HMMs to make the extracted patterns easier to interpret. However, these models are independently developed with their own training algorithm, so that we cannot combine multiple kinds of structures to build a PFSM for a specific application. In this paper, we prove that silent hidden Markov models (silent HMMs) are flexible models that have more expressive power than HSMMs and HHMMs. Silent HMMs are HMMs that contain silent states, which do not emit any observations. We show that we can obtain silent HMM equivalent to given HSMMs and HHMMs. We believe that these results form a firm foundation to use silent HMMs as a unified representation for PFSM modeling.","PeriodicalId":286427,"journal":{"name":"Finite-State Methods and Natural Language Processing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122635452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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