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Natural Language Processing for Search

  • Chapter
Web Information Retrieval

Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

Unstructured data, i.e., data that has not been created for computer usage, make up about 80 % of the entire amount of digital documents. Most of the time, unstructured data are textual documents written in natural language: clearly, this kind of data is a powerful information source that needs to be handled well. Access to unstructured data may be greatly improved with respect to traditional information retrieval methods by using deep language understanding methods. In this chapter, we provide a brief overview of the relationship between natural language processing and search applications. We describe some machine learning methods that are used for formalizing natural language problems in probabilistic terms. We then discuss the main challenges behind automatic text processing, focusing on question answering as a representative example of the application of various deep text processing techniques.

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Notes

  1. 1.

    Available at: alias-i.com/lingpipe.

  2. 2.

    www.cis.upenn.edu/~ace.

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Authors and Affiliations

  1. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy

    Stefano Ceri, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, Piero Fraternali & Silvia Quarteroni

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  1. Stefano Ceri
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  2. Alessandro Bozzon
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  3. Marco Brambilla
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  4. Emanuele Della Valle
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  5. Piero Fraternali
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  6. Silvia Quarteroni
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Ceri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., Quarteroni, S. (2013). Natural Language Processing for Search. In: Web Information Retrieval. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39314-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-39314-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39313-6

  • Online ISBN: 978-3-642-39314-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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