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Current Trends in Parsing Technology

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Trends in Parsing Technology

Part of the book series: Text, Speech and Language Technology ((TLTB,volume 43))

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Abstract

Significant advances in natural language processing require the development of adaptive syterns both for spoken and written language: systems that can interact naturally with human users, extend easily to new domains, produce readily usable translations of several languages, search the web rapidly and accurately, surnrnarise news coherently, and detect shifts in moods and emotions. Recent statistical datadriven techniques in natural language processing aim at acquiring the needed adaptivity by modelling the syntactic and lexical properties of large quantities of naturally occurring text.

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Authors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Paola Merlo

  2. Tilburg Center for Cognition and Communication (TiCC) and Department of Communication and Information Sciences, Tilburg University, Tilburg, The Netherlands

    Harry Bunt

  3. Uppsala University, Uppsala, Sweden

    Joakim Nivre

Authors
  1. Paola Merlo
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  2. Harry Bunt
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  3. Joakim Nivre
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Corresponding authors

Correspondence to Paola Merlo , Johan Hall , Ivan Titov , Kenji Sagae , Tetsuji Nakagawa , Massimiliano Ciaramita , Qin Iris Wang , Jason Eisner , Keith Hall , Detlef Prescher , Gertjan van Noord , Pierre Boullier , Yi Zhang , Takashi Ninomiya , Tadayoshi Hara or Rebecca Watson .

Editor information

Editors and Affiliations

  1. Tilburg University, Warandelaan 2, Tilburg, 5000 LE, Netherlands

    Harry Bunt

  2. Dépt. Linguistique, Université de Genève, rue de Candolle 2, Genève, 1211, Switzerland

    Paola Merlo

  3. Pimpstensvägen 16, Uppsala, 752 67, Sweden

    Joakim Nivre

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Merlo, P., Bunt, H., Nivre, J. (2010). Current Trends in Parsing Technology. In: Bunt, H., Merlo, P., Nivre, J. (eds) Trends in Parsing Technology. Text, Speech and Language Technology, vol 43. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9352-3_1

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