Titlebook: Natural Language Processing of Semitic Languages; Imed Zitouni Book 2014 Springer-Verlag Berlin Heidelberg 2014 Arabic, Hebrew, Syriac, Am

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Syntax and Parsing of Semitic Languagesways equally successful when applied to Semitic data. This chapter presents the syntax of Semitic languages and discusses how it challenges existing general-purpose parsing architectures. We then survey the different components of a generative probabilistic parsing system and show how they can be de

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Semantic Processing of Semitic Languagesntences, namely paradigmatic and syntagmatic models. We will contrast the processing of Semitic languages against English, illustrating some of the challenges – and clues – due to the inherent unique characteristics of Semitic languages.

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Statistical Machine Translatione discuss the special consideration that should be taken into account when developing SMT systems for Semitic languages. We discuss segmentation techniques for Semitic SMT; and finally we introduce a detailed guide on how to build an SMT using freely available resources.

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Anaphora ResolutionSummarization 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

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Question Answeringth the question and the answer are formulated in natural language. For this reason, in order to build an efficient QA system one has to rely on different NLP parsers to extract the necessary information to be used to compute the most relevant answer(s). Challenges are even higher when the target lan

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