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Anaphora Resolution

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Natural Language Processing of Semitic Languages

Abstract

Anaphora Resolution (AR) has attracted the attention of many researchers because of its relevance to Machine Translation, Information Retrieval, Text Summarization and many other applications. AR is a complicated problem in NLP especially in Semitic languages because of their complex morphological structure. Anaphora can be defined as a linguistic relation between two textual entities which is determined when a textual entity (the anaphor) refers to another entity of the text which usually occurs before it (the antecedent). The process of determining the antecedent of an anaphor is referred to as anaphora resolution. In this chapter, we present an account of the anaphora resolution task. The chapter consists of ten sections. The first section is an introduction to the problem. In the second section, we present different types of anaphora. Section 3 discusses the determinants and factors to anaphora resolution and its effect on increasing system performance. In section 4, we discuss the process of anaphora resolution. In section 5 we present different approaches to resolving anaphora and we discuss previous work in the field. Section 6 discusses the recent work in anaphora resolution, and section 7 discusses an important aspect in the anaphora resolution process which is the evaluation of AR systems. In sections 8 and 9, we focus on the anaphora resolution in Semitic languages in particular and the difficulties and challenges facing researchers. Finally, section 10 presents a summary of the chapter.

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Notes

  1. 1.

    ISA hierarchy, also called “is a” relationship, is an arrangement of items or objects in which the above item is represented as being the parent item for its derived items, and the derived items are represented as children for the above item. In Object Oriented, it means attributes inherited; i.e., if we declare A ISA B, every A entity is also considered to be a B entity. For example: if we have class A  = {person} and class B  = {male, female}. if B isa A, every entity in B is A, which means every male and female is a person.

  2. 2.

    The set of mentions contained in the gold standard, produced by a human expert, are referred to as TRUE or GOLD mentions, as opposed to the set of mentions contained in the system output, which are called SYSTEM or SYS mentions. Gold standard annotation is correctly identifying all NPs that are part of coreference chains.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Ahmed Zewel St., Giza, Egypt

    Khadiga Mahmoud Seddik

  2. Computational Linguistics Software Researcher, Networked Insights, 200 W. Adams Street, Chicago, IL, 60606, USA

    Ali Farghaly

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  1. Khadiga Mahmoud Seddik
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  2. Ali Farghaly
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Correspondence to Khadiga Mahmoud Seddik .

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  1. Microsoft, Redmond, Washington, USA

    Imed Zitouni

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Seddik, K.M., Farghaly, A. (2014). Anaphora Resolution. 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_8

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  • DOI: https://doi.org/10.1007/978-3-642-45358-8_8

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