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'''AlBERTo the first italian BERT model for Twitter languange understanding'''. Recent scientific studies on natural language processing (NLP) report the outstanding effectiveness observed in the use of context-dependent and task-free language understanding models such as ELMo, GPT, and BERT. Specifically, they have proved to achieve state-of-the-art performance in numerous complex NLP tasks such as question answering and sentiment analysis in the English language. Following the great popularity and effectiveness that these models are gaining in the scientific community, we trained a BERT language understanding model for the Italian language (AlBERTo). In particular, AlBERTo is focused on the language used in social networks, specifically on Twitter. To demonstrate its robustness, we evaluated AlBERTo on the EVALITA 2016 task SENTIPOLC (SENTIment POLarity Classification) obtaining state of the art results in subjectivity, polarity and irony detection on Italian tweets.

[[https://github.com/marcopoli/AlBERTo-it|AlBERTo]]

'''lesk-wsd-dsm''' is a software that implements a Word Sense Disambiguation algorithm based on the simple Lesk approach which integrates distributional semantics to compute the overlap between glosses.

Details about the algorithm are published in the following paper:

''P. Basile, A. Caputo, and G. Semeraro''. An Enhanced Lesk Word Sense Disambiguation algorithm through a Distributional Semantic Model. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 1591-1600.

[[https://github.com/pippokill/lesk-wsd-dsm|lesk-wsd-dsm on GitHub]]

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''P. Basile, M. Degemmis, A. Gentile, P. Lops, and G. Semeraro''. UNIBA:
JIGSAW Algorithm for Word Sense Disambiguation. In Proceedings of the 4th ACL

''P. Basile, M. Degemmis, A. Gentile, P. Lops, and G. Semeraro''. '''UNIBA:
JIGSAW Algorithm for Word Sense Disambiguation'''. In Proceedings of the 4th ACL

'''JIGSAW''' is a knowledge-based Word Sense Disambiguation system that attempts to disambiguate all words in a text by exploiting WordNet senses. The main assumption is that a specific strategy for each Part-Of-Speech is better than a single strategy.

'''JIGSAW''' JIGSAW is a knowledge-based Word Sense Disambiguation system that attempts to disambiguate all words in a text by exploiting WordNet senses. The main assumption is that a specific strategy for each Part-Of-Speech is better than a single strategy.

JIGSAW is able to disambiguate both Italian and English texts. More information are available in:

P. Basile, M. Degemmis, A. Gentile, P. Lops, and G. Semeraro. UNIBA:
JIGSAW Algorithm for Word Sense Disambiguation. In Proceedings of the 4th ACL
2007 International Workshop on Semantic Evaluations (SemEval-2007), pages 398–401.

[[https://github.com/pippokill/JIGSAW|JIGSAW on GitHub]]

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