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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing (Paperback)
Loot Price: R810
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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing (Paperback)
Series: Synthesis Lectures on Human Language Technologies
Expected to ship within 10 - 15 working days
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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 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.
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