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Unsupervised Information Extraction by Text Segmentation

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

In the literature, different approaches have been proposed to address the problem of extracting valuable data from the Web. In this chapter is presented an overview of such approaches. It begins by presenting a broad set of Web extraction methods and tools. Following a taxonomy previously used in the literature (Laender et al. 2002), they are divided into distinct groups according to their main approach. These groups are: Languages for Wrapper Development, Wrapper Induction Methods, NLP-based Methods, Ontology-based Methods, and HTML-aware Methods. Next, it is specifically presented probabilistic graph-based methods, supervised and unsupervised, and discusses their main characteristics in comparison to the unsupervised approach presented in this book.

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Authors and Affiliations

  1. Instituto de Computação, Universidade Federal do Amazonas, Manaus, AM, Brazil

    Eli Cortez & Altigran S. da Silva

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  1. Eli Cortez
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  2. Altigran S. da Silva
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Correspondence to Eli Cortez .

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Cortez, E., da Silva, A.S. (2013). Related Work. In: Unsupervised Information Extraction by Text Segmentation. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02597-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-02597-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02596-4

  • Online ISBN: 978-3-319-02597-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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