Paper: Learning Syntactic Categories Using Paradigmatic Representations of Word Context

ACL ID D12-1086
Title Learning Syntactic Categories Using Paradigmatic Representations of Word Context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

We 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 prop- erties of neighboring words. We compare a bigram based baseline model with several paradigmatic models and demonstrate signif- icant gains in accuracy. Our best model based on Euclidean co-occurrence embedding com- bines the paradigmatic context representation with morphological and orthographic features and achieves 80% many-to-one accuracy on a 45-tag 1M word corpus.