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Data Driven Models for Language Evolution: Cognate Identification and Phylogenetic Inference by PAM-like Matrices - Softcover
Languages that originate from a common ancestor are genetically related, words are the core of any language and cognates are words sharing the same ancestor and etymology. The evolutionary history of language, therefore, may be discovered by cognate identification and estimated by phylogenetic inference. Using several techniques originally designed for biological sequence analysis, an orthographic learning system for measuring string similarity has been developed and successfully applied to these tasks. Using PAM-like matrices, the system has outperformed the best comparable phonetic and orthographic cognate identification models previously reported in the literature, with results statistically significant and remarkably stable, regardless of the variation of the training dataset dimension. The method has also inferred high-quality Indo-European phylogenies, which are compatible with the benchmark tree and reproduce correctly all the established major language groups and subgroups present in the dataset. This book focuses on computational historical linguistics, but its contribution is also relevant to computational linguistics and natural language processing.
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Dr Antonella Delmestri was born in Trieste, Italy. She studied Computer Science at Udine University, Italy, and completed her doctorate in Information and Communication Technology at Trento University, Italy. She is currently an analyst programmer at Oxford University, U.K.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2011
- ISBN 10 3844395032
- ISBN 13 9783844395037
- BindingPaperback
- Number of pages180
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Data Driven Models for Language Evolution: Cognate Identification and Phylogenetic Inference by PAM-like Matrices
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Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Languages that originate from a common ancestor are genetically related, words are the core of any language and cognates are words sharing the same ancestor and etymology. The evolutionary history of language, therefore, may be discovered by cognate identification and estimated by phylogenetic inference. Using several techniques originally designed for biological sequence analysis, an orthographic learning system for measuring string similarity has been developed and successfully applied to these tasks. Using PAM-like matrices, the system has outperformed the best comparable phonetic and orthographic cognate identification models previously reported in the literature, with results statistically significant and remarkably stable, regardless of the variation of the training dataset dimension. The method has also inferred high-quality Indo-European phylogenies, which are compatible with the benchmark tree and reproduce correctly all the established major language groups and subgroups present in the dataset. This book focuses on computational historical linguistics, but its contribution is also relevant to computational linguistics and natural language processing. 180 pp. Englisch. Seller Inventory # 9783844395037
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Data Driven Models for Language Evolution Cognate Identification and Phylogenetic Inference by PAMlike Matrices
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Data Driven Models for Language Evolution Cognate Identification and Phylogenetic Inference by PAMlike Matrices
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Book Description Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Languages that originate from a common ancestor are genetically related, words are the core of any language and cognates are words sharing the same ancestor and etymology. The evolutionary history of language, therefore, may be discovered by cognate identification and estimated by phylogenetic inference. Using several techniques originally designed for biological sequence analysis, an orthographic learning system for measuring string similarity has been developed and successfully applied to these tasks. Using PAM-like matrices, the system has outperformed the best comparable phonetic and orthographic cognate identification models previously reported in the literature, with results statistically significant and remarkably stable, regardless of the variation of the training dataset dimension. The method has also inferred high-quality Indo-European phylogenies, which are compatible with the benchmark tree and reproduce correctly all the established major language groups and subgroups present in the dataset. This book focuses on computational historical linguistics, but its contribution is also relevant to computational linguistics and natural language processing. Seller Inventory # 9783844395037
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Book Description Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Delmestri AntonellaDr Antonella Delmestri was born in Trieste, Italy. She studied Computer Science at Udine University, Italy, and completed her doctorate in Information and Communication Technology at Trento University, Italy. She i. Seller Inventory # 5476978
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