Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

Häftad, Engelska, 2013

Del i serien Synthesis Lectures on Human Language Technologies

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2013-05-22
Mått:191 x 235 x 7 mm
Vikt:212 g
Format:Häftad
Språk:Engelska
Serie:Synthesis Lectures on Human Language Technologies
Antal sidor:93
Förlag:Springer International Publishing AG
ISBN:9783031010217
Originaltitel:Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

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Mer om författaren

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.

Innehållsförteckning

Introduction.- Supervised and Unsupervised Prediction.- Semi-Supervised Learning.- Learning under Bias.- Learning under Unknown Bias.- Evaluating under Bias.