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A lot of digital ink has been spilled on "big data" over the past
few years. Most of this surge owes its origin to the various types
of unstructured data in the wild, among which the proliferation of
text-heavy data is particularly overwhelming, attributed to the
daily use of web documents, business reviews, news, social posts,
etc., by so many people worldwide.A core challenge presents itself:
How can one efficiently and effectively turn massive, unstructured
text into structured representation so as to further lay the
foundation for many other downstream text mining applications? In
this book, we investigated one promising paradigm for representing
unstructured text, that is, through automatically identifying
high-quality phrases from innumerable documents. In contrast to a
list of frequent n-grams without proper filtering, users are often
more interested in results based on variable-length phrases with
certain semantics such as scientific concepts, organizations,
slogans, and so on. We propose new principles and powerful
methodologies to achieve this goal, from the scenario where a user
can provide meaningful guidance to a fully automated setting
through distant learning. This book also introduces applications
enabled by the mined phrases and points out some promising research
directions.
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