In contrast to existing algorithms developed for this problem, our algorithm is designed to (1) operate under a resource-scarce setting in which no languagespecific tools or resources are available and (2) more tightly integrate morphological information with the distributional POS induction framework.
our algorithm (1) improves the quality of the seed clusters by employing seed words that are distributionally and morphologically reliable and (2) uses support vector learning to combine morphological and distributional information.