Overview
- The book sets an ambitious goal: to shift development of language processing systems to a much more automated setting than previous works
- A new approach is defined
- All software described is open source and freely available ?
- Includes supplementary material: sn.pub/extras
Part of the book series: Theory and Applications of Natural Language Processing (NLP)
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Table of contents (8 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Structure Discovery in Natural Language
Authors: Chris Biemann
Series Title: Theory and Applications of Natural Language Processing
DOI: https://doi.org/10.1007/978-3-642-25923-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-25922-7Published: 09 December 2011
Softcover ISBN: 978-3-642-44230-8Published: 01 March 2014
eBook ISBN: 978-3-642-25923-4Published: 08 December 2011
Series ISSN: 2192-032X
Series E-ISSN: 2192-0338
Edition Number: 1
Number of Pages: XX, 180
Topics: Artificial Intelligence, Computational Linguistics, Graph Theory