Current language technology is dominated by approaches that
either enumerate a large set of rules, or are focused on a large
amount of manually labelled data. The creation of both is
time-consuming and expensive, which is commonly thought to be the
reason why automated natural language understanding has still not
made its way into "real-life" applications yet.
This book sets an ambitious goal: to shift the development of
language processing systems to a much more automated setting than
previous works. A new approach is defined: what if computers
analysed large samples of language data on their own, identifying
structural regularities that perform the necessary abstractions and
generalisations in order to better understand language in the
process?
After defining the framework of Structure Discovery and shedding
light on the nature and the graphic structure of natural language
data, several procedures are described that do exactly this: let
the computer discover structures without supervision in order to
boost the performance of language technology applications. Here,
multilingual documents are sorted by language, word classes are
identified, and semantic ambiguities are discovered and resolved
without using a dictionary or other explicit human input. The book
concludes with an outlook on the possibilities implied by this
paradigm and sets the methods in perspective to human computer
interaction.
The target audience are academics on all levels (undergraduate
and graduate students, lecturers and professors) working in the
fields of natural language processing and computational
linguistics, as well as natural language engineers who are seeking
to improve their systems.
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