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Anders Søgaard

Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

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This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both readable by first-year students and interesting t… Mehr

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Produktdetails


  • ISBN: 978-3-031-01021-7
  • EAN: 9783031010217
  • Produktnummer: 39048783
  • Verlag: Springer International Publishing
  • Sprache: Englisch
  • Erscheinungsjahr: 2013
  • Seitenangabe: 104 S.
  • Masse: H23.5 cm x B19.1 cm x D0.5 cm 212 g
  • Abbildungen: Paperback
  • Gewicht: 212

Über den Autor


Anders Søgaard is a father of three and a published poet, as well as a Full Professor in Computer Science the University of Copenhagen. He is currently funded by the Novo Nordisk Foundation, the Lundbeck Foundation, and the Innovation Fund Denmark; before that, he held an ERC Starting Grant and a Google Focused Research Award. He has won best paper awards at NAACL, EACL, CoNLL, etc. He previously wrote Semi-Supervised Learning and Domain Adaptation in NLP (Morgan & Claypool, 2013) and Cross-Lingual Word Embeddings (Morgan & Claypool, 2019), the latter with co-authors Ivan Vulic, Sebastian Ruder, and Manaal Faruqui.

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