For increased speed in developing gigaword language resources for medium resource density languages we integrated several FOSS tools in the HUN* toolkit. While the speed and efficiency of the resulting pipeline has surpassed our expectations, our experience in developing LDC-style resource packages for Uzbek and Kurdish makes clear that neither the data collection nor the subsequent processing stages can be fully automated.

The paper presents an evaluation of maxent POS disambiguation systems that incorporate an open source morphological analyzer to constrain the probabilistic models. The experiments show that the best proposed architecture, which is the first application of the maximum entropy framework in a Hungarian NLP task, outperforms comparable state of the art tagging methods and is able to handle out of vocabulary items robustly, allowing for efficient analysis of large (web-based) corpora.

This paper describes morphdb.hu, a Hungarian lexical database and morphological grammar. Morphdb.hu is the outcome of a several-year collaborative effort and represents the resource with the widest coverage and broadest range of applicability presently available for Hungarian. The grammar resource is the formalization of well-founded theoretical decisions handling inflection and productive derivation. The lexical database was created by merging three independent lexical databases, and the resulting resource was further extended.