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 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.
REF considers vector paradigmatic representations of words along with morphological and orthographic similarities as features and used a modified k-means clustering to determine syntactic categories.
Learning Syntactic Categories Using Paradigmatic Representations of Word Context
EMNLP
Computer Science