Structure Discovery in Natural Language. 1st ed. 2012

種類:

電子ブック

責任表示:

by Chris Biemann

出版情報:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012

著者名:

• Biemann, Chris.
• SpringerLink (Online service)

シリーズ名:

Theory and Applications of Natural Language Processing ;

ISBN:

9783642259234 [3642259235]    

注記:

Foreword by Antal van den Bosch -- 1.Introduction -- 2.Graph Models -- 3.SmallWorlds of Natural Language -- 4.Graph Clustering -- 5.Unsupervised Language Separation -- 6.Unsupervised Part-of-Speech Tagging -- 7.Word Sense Induction and Disambiguation -- 8.Conclusion -- References .
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

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