Unsupervised Learning of Morphology by Using Syntactic Categories
Burcu Can, Suresh Manandhar · CLEF (Working Notes) · 2009
This paper presents a method for unsupervised learning of morphology that exploits the syntactic categories of words. Previous research [4][12] on learning of morphology and syntax has shown that both kinds of knowledge affect each other making it possible to use one type of knowledge to help the other. In this work, we make use of syntactic information i.e. Part-of-Speech (PoS) tags of words to aid morphological analysis. We employ an existing unsupervised PoS tagging algorithm for inducing the PoS categories. A distributional clustering algorithm is developed for inducing morphological paradigms.