AnIta: a powerful morphological analyser for Italian

13930

Authors

nan

Abstract

AbstractIn this paper we present AnIta, a powerful morphological analyser for Italian implemented within the framework of finite-state-automata models. It is provided by a large lexicon containing more than 110,000 lemmas that enable it to cover relevant portions of Italian texts. We describe our design choices for the management of inflectional phenomena as well as some interesting new features to explicitly handle derivational and compositional processes in Italian, namely the wordform segmentation structure and Derivation Graph. Two different evaluation experiments, for testing coverage (Recall) and Precision, are described in detail, comparing the AnIta performances with some other freely available tools to handle Italian morphology. The experiments results show that the AnIta Morphological Analyser obtains the best performances among the tested systems, with Recall = 97.21% and Precision = 98.71%. This tool was a fundamental building block for designing a performant PoS-tagger and Lemmatiser for the Italian language that participated to two EVALITA evaluation campaigns ranking, in both cases, together with the best performing systems.

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Labeled Text

AbstractIn this paper we present AnIta, a powerful morphological analyser for Italian implemented within the framework of finite-state-automata models. It is provided by a large lexicon containing more than 110,000 lemmas that enable it to cover relevant portions of Italian texts. We describe our design choices for the management of inflectional phenomena as well as some interesting new features to explicitly handle derivational and compositional processes in Italian, namely the wordform segmentation structure and Derivation Graph. Two different evaluation experiments, for testing coverage (Recall) and Precision, are described in detail, comparing the AnIta performances with some other freely available tools to handle Italian morphology. The experiments results show that the AnIta Morphological Analyser obtains the best performances among the tested systems, with Recall = 97.21% and Precision = 98.71%. This tool was a fundamental building block for designing a performant PoS-tagger and Lemmatiser for the Italian language that participated to two EVALITA evaluation campaigns ranking, in both cases, together with the best performing systems.

Publication Details

Paper ID:
13930
Year:
2012
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan