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A Frequency Dictionary of Russian von Sharoff, Serge

core vocabulary for learners
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Hersteller: Taylor and Francis
ISBN: 978-0-415-52141-3
Verfügbarkeit: Nicht (mehr) im Sortiment/Besorgung möglich
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A Frequency Dictionary of Russian is an invaluable tool for all learners of Russian, providing a list of the 5,000 most frequently used words in the language and the 300 most frequent multiword constructions. The dictionary is based on data from a 150-million-word internet corpus taken from more than 75,000 webpages and covering a range of text types from news and journalistic articles, research papers, administrative texts and fiction. All entries in the rank frequency list feature the English equivalent, a sample sentence with English translation, a part of speech indication, indication of stress for polysyllabic words and information on inflection for irregular forms. The dictionary also contains twenty-six thematically organised and frequency-ranked lists of words on a variety of topics, such as food and drink, travel, and sports and leisure. A Frequency Dictionary of Russian enables students of all levels to get the most out of their study of vocabulary in an engaging and efficient way. It is also a rich resource for language teaching, research, curriculum design, and materials development. A CD version is available to purchase separately. Designed for use by corpus and computational linguists it provides the full text in a format that researchers can process and turn into suitable lists for their own research purposes.
Autor Sharoff, Serge / Umanskaya, Elena / Wilson, James
Verlag Taylor and Francis
Einband Fester Einband
Erscheinungsjahr 2013
Seitenangabe 400 S.
Meldetext Nicht (mehr) im Sortiment/Besorgung möglich
Ausgabekennzeichen Englisch
Abbildungen Farb., s/w. Abb.
Masse H24.6 cm x B17.4 cm 900 g
Coverlag Routledge (Imprint/Brand)
Reihe Routledge Frequency Dictionaries
Erscheinungstermin 20130321
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Über den Autor Sharoff, Serge

Serge Sharoff, Ph.D.,  is Professor of Language Technology and Digital Humanities at the Centre for Translation Studies, University of Leeds. His research focuses on Natural Language Processing, including automated methods for collecting very large corpora from the Web, their analysis in terms of domains, genres or text quality, as well as extraction of lexicons and terminology from corpora. The application domains for this kind of research in the Digital Humanities include text annotation, information retrieval, machine translation and computer-assisted language learning. His research stresses the inherent multilingualism of NLP, which implies that tools and resources can be ported across languages by paying attention to the respective linguistic properties.Pierre Zweigenbaum, Ph.D., FACMI, FIAHSI, is a Senior Researcher at the Interdisciplinary Laboratory for Digital Sciences (LISN, Orsay, France), a laboratory of the French National Center forScientific Research (CNRS) and Université Paris-Saclay, where he has led the ILES Natural Language Processing group. Before CNRS he was a researcher at Paris Public Hospitals in an Inserm team. He also was a part-time professor at the National Institute for Oriental Languages and Civilizations.  His research focus is Natural Language Processing, with medicine as a main application domain. He has also designed methods to acquire linguistic knowledge automatically from corpora and thesauri, to help extend monolingual and bilingual lexicons and terminologies, using parallel and comparable corpora.Reinhard Rapp, Ph.D., is Professor of Applied Translation Studies at Magdeburg-Stendal University of Applied Sciences and is also affiliated with the University of Mainz. He has conducted EU-funded research projects at the University of Geneva, the University of Tarragona, the University of Leeds, at Aix-Marseille University, at the University of Mainz and at the Athena Research Center in Athens. His main research interests are in computational linguistics, translation studies and cognitive science. His publications have dealt with unsupervised language learning from text corpora, word sense disambiguation, text mining, thesaurus construction, bilingual dictionary induction from parallel and comparable corpora, and with statistical and neural machine translation. 

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