Semantic argument classification and semantic categorization of Turkish existential sentences using support vector learning
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There are three types of sentences that form all existing natural languages: verbal sentences (e.g. “I read the book.”), copulative sentences (e.g. “The book is on the table.”), and existential sentences (e.g. “There is a book on the table.”). Syntactic and semantic recognition of these sentence types are crucially important in computational linguistics although there has not been any significant work towards this end. This thesis, in an attempt to fill this evident gap, is on identifying and assigning semantic categories of Turkish existential sentences in print. Existential sentences in Turkish are minimally characterized by the two existential particles var, meaning there is/are, and yok, meaning there is/are no. In addition to these most basic meanings, other senses of existential particles are possible, which can be categorized into groups such as case existentials and possession existentials. Our system does shallow semantic parsing in defining the predicate-argument relationships in an existential sentence on a word-byword basis, via utilizing Support Vector Machines, after which it proceeds with the semantic categorization of the whole sentence. For both of these tasks, our system produces promising results, in terms of accuracy and precision/recall, respectively. Part of this research contributes to the annotation of the METU-Sabancı Turkish Treebank with semantic information.
Keywordsshallow semantic parsing
Turkish existential sentences
support vector machines
semantic role labeling