Italian Lemmatization by Rules with Getaruns

Abstract. We present an approach to lemmatization based on exhaustive morphological analysis and use of external knowledge sources to help disambiguation which is the most relevant issue to cope with. Our system GETARUNS was not concerned with lemmatization directly and used morphological analysis only as backoff solution in case the word was not retrieved in the wordform dictionaries available. We found out that both the rules and the root dictionary needed amending. This was started during development and before testset was distributed, but not completed for lack of time. Thus the task final results only depict an incomplete system, which has now eventually come to a complete version with rather different outcome. We moved from 98.42 to 99.82 in the testset and from 99.85 to 99.91 in the devset. As said above, this is produced by rules and is not subject to statistical evaluation which may change according to different training sets