Negative concord in Levantine Arabic

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Hoyt, Frederick MacNeill

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This dissertation is a study of negative concord in Levantine Arabic (Israel/Palestine, Jordan, Lebanon, Syria), where negative concord is the failure of an n-word to express negative meaning distinctly when in syntagm with another negative expression . A set of n-words is identified, including the never-words <ʔɛbadan> and <bɪlmarra> "never, not once, not at all," the negative minimizers and <qɛšal> "nothing," and the negative scalar focus particle "not (even) (one), not a (single)." Each can be used to express negation in sentence fragments and other constructions with elliptical interpretations, such as gapping and coordination. Beyond that, the three categories differ syntactically and semantically. I present analyses of these expressions that treat them as having different morphological and semantic properties. The data support an ambiguity analysis for wala-phrases, and a syntactic analysis of it with never-words, indicating that a single, uniform theory of negative concord should be rejected for Levantine Arabic.

The dissertation is the first such work to explicitly identify negative concord in Levantine Arabic, and to provide a detailed survey and analysis of it. The description includes subtle points of variation between regional varieties of Levantine, as well as in depth analysis of the usage of n-words. It also adds a large new data set to the body of data that has been reported on negative concord, and have several implications for theories on the subject. The dissertation also makes a contribution to computational linguistics as applied to Arabic, because the analyses are couched in Combinatory Categorial Grammar, a formalism that is used both for linguisic theorizing as well as for a variety of practical applications, including text parsing and text generaration. The semantic generalizations reported here are also important for practical computational tasks, because they provide a way to correctly calculate the negative or positive polarity of utterances in a negative concord language, which is essential for computational tasks such as machine translation or sentiment analysis.




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