Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A morphosyntactic Brill Tagger for inflectional languages
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
MULTEXT-East: morphosyntactic resources for Central and Eastern European languages
Language Resources and Evaluation
Verb analysis in a highly inflective language with an MFF algorithm
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
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We address the problem of morpho-syntactic disambiguation of arbitrary texts in a highly inflectional natural language. We use a large tagset (615 tags), EAGLES and MULTEXT compliant [5]. The large tagset is internally mapped onto a reduced one (82 tags), serving statistical disambiguation, and a text disambiguated in terms of this tagset is subsequently subject to a recovery process of all the information left out from the large tagset. This two step process is called tiered tagging. To further improve the tagging accuracy we use a combined language models classifier, a procedure that interpolates the results of tagging the same text with several register-specific language models.