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Machine Learning for Text pp 381–411Cite as

Information Extraction

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Abstract

In its most basic form, text is a sequence of tokens, which is not annotated with the properties of these tokens. The goal of information extraction is to discover specific types of useful properties of these tokens and their interrelationships relationships.

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Notes

  1. 1.

    The bibliographic notes contain pointers to such methods.

  2. 2.

    https://en.wikipedia.org/wiki/Help:Infobox.

  3. 3.

    https://en.wikipedia.org/wiki/John_F._Kennedy.

  4. 4.

    This early series of conferences played an important role in the evolution of the field of information extraction, which had been largely sporadic till then.

  5. 5.

    Note that this approach is slightly different from directly tagging the last token of an entity that a state that indicates its end point. In the approach of the previous section, a person entity would be ended by a token with a PE tag, whereas Nymble simply uses a separate “end” token.

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  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Charu C. Aggarwal

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Aggarwal, C.C. (2018). Information Extraction. In: Machine Learning for Text. Springer, Cham. https://doi.org/10.1007/978-3-319-73531-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-73531-3_12

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