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001 978-3-031-02149-7
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008 220601s2013 sz | s |||| 0|eng d
020 _a9783031021497
_9978-3-031-02149-7
024 7 _a10.1007/978-3-031-02149-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSøgaard, Anders.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983309
245 1 0 _aSemi-Supervised Learning and Domain Adaptation in Natural Language Processing
_h[electronic resource] /
_cby Anders Søgaard.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aX, 93 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aIntroduction -- Supervised and Unsupervised Prediction -- Semi-Supervised Learning -- Learning under Bias -- Learning under Unknown Bias -- Evaluating under Bias.
520 _aThis 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 to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_983314
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010217
776 0 8 _iPrinted edition:
_z9783031032776
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_983315
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02149-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85483
_d85483