A resource-light approach to morpho-syntactic tagging /
Feldman, Anna.
A resource-light approach to morpho-syntactic tagging / Anna Feldman and Jirka Hana. - 1 online resource (xiv, 185 pages) : illustrations. - Language and computers : studies in practical linguistics ; no. 70 . - Language and Computers 70. Rodopi Language and Linguistics Special E-Book Collection, 2007-2014, ISBN: 9789004357983. .
Includes bibliographical references (p. 133-148) and index.
Preliminary Material -- Introduction -- Common tagging techniques -- Previous resource-light approaches to NLP -- Languages, corpora and tagsets -- Quantifying language properties -- Resource-light morphological analysis -- Cross-language morphological tagging -- Summary and further work -- Bibliography -- Tagsets we use -- Corpora -- Language properties -- Citation Index.
Available to subscribing member institutions only.
While supervised corpus-based methods are highly accurate for different NLP tasks, including morphological tagging, they are difficult to port to other languages because they require resources that are expensive to create. As a result, many languages have no realistic prospect for morpho-syntactic annotation in the foreseeable future. The method presented in this book aims to overcome this problem by significantly limiting the necessary data and instead extrapolating the relevant information from another, related language. The approach has been tested on Catalan, Portuguese, and Russian. Although these languages are only relatively resource-poor, the same method can be in principle applied to any inflected language, as long as there is an annotated corpus of a related language available. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive, manually created resources: days instead of years. This book touches upon a number of topics: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and Natural Language Processing (NLP). Researchers and students who are interested in these scientific areas as well as in cross-lingual studies and applications will greatly benefit from this work. Scholars and practitioners in computer science and linguistics are the prospective readers of this book.
9789042027695
10.1163/9789042027695 DOI
Grammar, Comparative and general--Morphosyntax.
Computational linguistics.
Language transfer (Language learning)
Cross-language information retrieval.
Computational linguistics.
Cross-language information retrieval.
Grammar, Comparative and general--Morphosyntax.
Language transfer (Language learning)
P290 / .F45 2010eb
415
A resource-light approach to morpho-syntactic tagging / Anna Feldman and Jirka Hana. - 1 online resource (xiv, 185 pages) : illustrations. - Language and computers : studies in practical linguistics ; no. 70 . - Language and Computers 70. Rodopi Language and Linguistics Special E-Book Collection, 2007-2014, ISBN: 9789004357983. .
Includes bibliographical references (p. 133-148) and index.
Preliminary Material -- Introduction -- Common tagging techniques -- Previous resource-light approaches to NLP -- Languages, corpora and tagsets -- Quantifying language properties -- Resource-light morphological analysis -- Cross-language morphological tagging -- Summary and further work -- Bibliography -- Tagsets we use -- Corpora -- Language properties -- Citation Index.
Available to subscribing member institutions only.
While supervised corpus-based methods are highly accurate for different NLP tasks, including morphological tagging, they are difficult to port to other languages because they require resources that are expensive to create. As a result, many languages have no realistic prospect for morpho-syntactic annotation in the foreseeable future. The method presented in this book aims to overcome this problem by significantly limiting the necessary data and instead extrapolating the relevant information from another, related language. The approach has been tested on Catalan, Portuguese, and Russian. Although these languages are only relatively resource-poor, the same method can be in principle applied to any inflected language, as long as there is an annotated corpus of a related language available. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive, manually created resources: days instead of years. This book touches upon a number of topics: typology, morphology, corpus linguistics, contrastive linguistics, linguistic annotation, computational linguistics and Natural Language Processing (NLP). Researchers and students who are interested in these scientific areas as well as in cross-lingual studies and applications will greatly benefit from this work. Scholars and practitioners in computer science and linguistics are the prospective readers of this book.
9789042027695
10.1163/9789042027695 DOI
Grammar, Comparative and general--Morphosyntax.
Computational linguistics.
Language transfer (Language learning)
Cross-language information retrieval.
Computational linguistics.
Cross-language information retrieval.
Grammar, Comparative and general--Morphosyntax.
Language transfer (Language learning)
P290 / .F45 2010eb
415