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-of-speech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.
|Title of host publication||Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)|
|Place of Publication||Baltimore, Maryland|
|Publisher||Association for Computational Linguistics|
|Number of pages||7|
|Publication status||Published - 1 Jun 2014|