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Relation Extraction

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Natural Language Processing of Semitic Languages

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Abstract

We discuss the problem of extracting semantic relations between entities from text. We concentrate on types of relations that belong to predefined classes, and we specifically address how to detect relations explicitly described in the text. We describe three main approaches to relation extraction: using supervised (statistical) feature-based classifiers, using supervised kernel-based classifiers, and using semi-supervised methods. Supervised methods need a large collection of manually labeled examples to learn how to detect relations, while semi-supervised methods need a moderately large collection of manually labeled examples as well as a large number of unlabeled examples. We then address the language-specific difficulties that arise when extracting relations from semitic languages, and discuss the impact of the lact of diacritics as well as the challenges posed by complex morphology. Finally, we analyze in detail a specific system specifically trained to detect relations in Arabic text, and review its performance on the 2004 ACE relation detection task.

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Authors and Affiliations

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

    Vittorio Castelli

  2. Microsoft, Redmond, WA, USA

    Imed Zitouni

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  1. Vittorio Castelli
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  2. Imed Zitouni
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Correspondence to Vittorio Castelli .

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  1. Microsoft, Redmond, Washington, USA

    Imed Zitouni

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Castelli, V., Zitouni, I. (2014). Relation Extraction. In: Zitouni, I. (eds) Natural Language Processing of Semitic Languages. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45358-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-45358-8_9

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