EAN: 9783031010217

SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGI...

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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