Fb2 Structure Discovery in Natural Language (Theory and Applications of Natural Language Processing) ePub
by Antal van den Bosch,Chris Biemann
|Subcategory:||Technologies and Computers|
|Author:||Antal van den Bosch,Chris Biemann|
|Publisher:||Springer; 2012 edition (December 8, 2011)|
|Fb2 eBook:||1868 kb|
|ePub eBook:||1972 kb|
|Digital formats:||lrf doc mobi lit|
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Structure Discovery in N.
Chris Biemann (Author), Antal van den Bosch (Foreword). ISBN-13: 978-3642259227.
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Structure-based drug discovery methods have been transformed in the last 5-10 years and are now having a major impact on the discovery of new drugs.
Foreword by Antal van den Bosch
Foreword by Antal van den Bosch. Chris Biemann Computer Science Department Technische Universität Darmstadt Hochschulstr. 10 64289 Darmstadt Germany.
In this chapter, the contributions of this book are summarised and put in to a larger perspective.
Antal van den Bosch, Universität Tilburg 2. Prof. Dr. Gerhard Heyer, Universität Leipzig 3.
Chapter 2. Graph Models
Autor: Chris Biemann. Verlag: Springer Berlin Heidelberg. Chapter 2. Graph Models.
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.