We present a new approach to inducing the syntactic categories of words, combining their distributional and morphological properties in a joint nonparametric Bayesian model based on the distance-dependent Chinese Restaurant Process. The prior distribution over word clusterings uses a log-linear model of morphological similarity; the likelihood function is the probability of generating vector word embeddings. The weights of the morphology model are learned jointly while inducing part-ofspeech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.
Kairit et al. REF present an approach for inducing POS classes by combining morphological and distributional information in non-parametric Bayesian generative model based on distance-dependent Chinese restaurant process.
POS induction with distributional and morphological information using a distance-dependent Chinese restaurant process
ACL
Computer Science