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Structure Discovery in Natural Language (Theory and Applications of Natural Language Processing) - Hardcover
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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.
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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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Hardcover. Zustand: new. Hardcover. 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. Current language technology is dominated by time-consuming approaches that either enumerate a large set of rules, or focus on a large amount of manually labeled data. This volume advocates a new open-source methodology that is much more automated. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783642259227
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 200 pp. Englisch. Bestandsnummer des Verkäufers 9783642259227
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Zustand: New. Current language technology is dominated by time-consuming approaches that either enumerate a large set of rules, or focus on a large amount of manually labeled data. This volume advocates a new open-source methodology that is much more automated. Series: Theory and Applications of Natural Language Processing. Num Pages: 200 pages, biography. BIC Classification: CFX; UYQ. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 12. Weight in Grams: 467. . 2011. 2012. Hardback. . . . . Bestandsnummer des Verkäufers V9783642259227
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