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Context in Computing pp 143–154Cite as

Context and NLP

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Abstract

Early Natural Language Processing (NLP) endeavors often employed contextual cues as supplemental assistive measures—secondary sources of data to help understand its users’ linguistic inputs. Context was used more as a tie-breaking tool rather than as a central component in conversational negotiation. Recent work in context-based reasoning has inspired a paradigm shift from these context-assisted techniques to context-centric NLP systems. This evolution of context’s role in NLP is necessary to support today’s sophisticated Human-Computer Interaction (HCI) applications, such as personal digital assistants, language tutors, and question answering systems. In these applications, there is a strong sense of utilitarian, purpose-driven conversation. Such an emphasis on goal-oriented behavior requires that the underlying NLP methods be capable of navigating through a conversation at the conceptual, or contextual level. This chapter explores the natural bond between NLP and context-based methods, as it manifests itself in the context-centric paradigm. Insights and examples are provided along the way to shed light on this evolved way of engineering natural language-based HCI.

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Authors and Affiliations

  1. Intelligent Systems Laboratory, University of Central Florida, Orlando, FL 32816, USA

    Victor Hung

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  1. Victor Hung
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Correspondence to Victor Hung .

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Editors and Affiliations

  1. University Pierre and Marie Curie (UPMC), Paris, France

    Patrick Brézillon

  2. University of Central Florida, Orlando, Florida, USA

    Avelino J. Gonzalez

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Hung, V. (2014). Context and NLP. In: Brézillon, P., Gonzalez, A. (eds) Context in Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1887-4_10

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  • DOI: https://doi.org/10.1007/978-1-4939-1887-4_10

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