This book presents a comprehensive overview of semi-supervised
approaches to dependency parsing. Having become increasingly
popular in recent years, one of the main reasons for their success
is that they can make use of large unlabeled data together with
relatively small labeled data and have shown their advantages in
the context of dependency parsing for many languages. Various
semi-supervised dependency parsing approaches have been proposed in
recent works which utilize different types of information gleaned
from unlabeled data. The book offers readers a comprehensive
introduction to these approaches, making it ideally suited as a
textbook for advanced undergraduate and graduate students and
researchers in the fields of syntactic parsing and natural language
processing.
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