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2018 | OriginalPaper | Buchkapitel

Turkish Document Classification with Coarse-Grained Semantic Matrix

verfasst von : İlknur Dönmez, Eşref Adalı

Erschienen in: Computational Linguistics and Intelligent Text Processing

Verlag: Springer International Publishing

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Abstract

In this paper, we present a novel method for Document Classification that uses semantic matrix representation of Turkish sentences by concentrating on the sentence phrases and their concepts in text. Our model has been designed to find phrases in a sentence, identify their relations with specific concepts, and represent the sentences as coarse-grained semantic matrix. Predicate features and semantic class type are also added to the coarse-grained semantic matrix representation. The highest success rate in Turkish Document Classification “97.12” is obtained by adding the coarse-grained semantic matrix representation to the data which has previous highest result in the previous studies about Turkish Document Classification.

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Titel: Turkish Document Classification with Coarse-Grained Semantic Matrix
verfasst von: İlknur Dönmez
Eşref Adalı
Verlag: Springer International Publishing
Buch: Computational Linguistics and Intelligent Text Processing
Print ISBN: 978-3-319-75486-4

Electronic ISBN: 978-3-319-75487-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-75487-1_37

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