Abstract. This paper presents PurePos, a new open source Hidden Markov model based morphological tagger tool that has an interface to an integrated morphological analyzer and thus performs full disambiguated morphological analysis including lemmatization of words both known and unknown to the morphological analyzer. The tagger is implemented in Java and has a permissive LGPL license thus it is easy to integrate and modify. It is fast to train and use while having an accuracy on par with slow to train Maximum Entropy or Conditional Random Field based taggers. Full integration with morphology and an incremental training feature make it suited for integration in web based applications. We show that the integration with morphology boosts our tool's accuracy in every respect -especially in full morphological disambiguation -when used for morphologically complex agglutinating languages. We evaluate PurePos on Hungarian data demonstrating its state-of-the-art performance in terms of tagging precision and accuracy of full morphological analysis.
Recently, PurePOS has been implemented REF , which is an open source morphological tagger based on a Hidden Markov Model.
PurePos: An Open Source Morphological Disambiguator
Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science
Proceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science
Biology