Wachsmuth Henning (22 results)

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This Open Access book provides an essential overview of the topic of argumentation mining, an active research area of Natural Language Processing (NLP). Argument mining can automatically find and collect arguments published in newspapers, social media, or elsewhere. Argument mining can be also used to give a precise account of the argumentative structure of a single text or dialogue such as a legal or scientific document, a student essay, or an online conversation. Increasingly, computational approaches provide synopses of arguments and compare the relative strength of arguments. In this book, the authors start with basic argumentation concepts and describe the NLP methods for mining arguments automatically from natural language text. The authors then show how NLP is used for assessing the quality of arguments and generating new arguments. This Second Edition presents an updated survey of the state of the art of argument mining, providing increased depth and insights into many more NLP tasks than the prior edition. This new edition discusses the disruptive changes that have come with large language models and highlights potential future developments. The authors also update the treatment of the common steps in argument mining, argument generation tasks and methods, and computational argumentation applications. Completely new chapters cover perspective assessment, argument quality assessment, and argument reasoning.…

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Taschenbuch. Condition: Neu. Text Analysis Pipelines | Towards Ad-hoc Large-Scale Text Mining | Henning Wachsmuth | Taschenbuch | Lecture Notes in Computer Science | xx | Englisch | 2015 | Springer | EAN 9783319257402 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics.Both web search and big data analytics aim to fulfill peoples' needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines. 324 pp. Englisch.…

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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. .…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics.Both web search and big data analytics aim to fulfill peoples' needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics.Both web search and big data analytics aim to fulfill peoples¿ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 324 pp. Englisch.…