Researcher: Sert, Enis Rıfat
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Sert, Enis Rıfat
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Publication Metadata only Learning syntactic categories using paradigmatic representations of word context(Association for Computational Linguistics, 2012) Department of Computer Engineering; Department of Computer Engineering; Yüret, Deniz; Yatbaz, Mehmet Ali; Sert, Enis Rıfat; Faculty Member; PhD Student; Master Student; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 179996; N/A; N/AWe investigate paradigmatic representations of word context in the domain of unsupervised syntactic category acquisition. Paradigmatic representations of word context are based on potential substitutes of a word in contrast to syntagmatic representations based on properties of neighboring words. We compare a bigram based baseline model with several paradigmatic models and demonstrate significant gains in accuracy. Our best model based on Euclidean co-occurrence embedding combines the paradigmatic context representation with morphological and orthographic features and achieves 80% many-to-one accuracy on a 45-tag 1M word corpus.