In this paper, we propose a Deep Learning architecture for several Italian Natural language processing tasks based on a state of the art model that exploits both word- and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture provided state of the art performance in several sequence labeling tasks for the English language. We exploit the same approach for the Italian language and extend it for performing a multi-task learning involving PoS-tagging and sentiment analysis. Results show that the system is able to achieve state of the art performance in all the tasks and in some cases overcome the best systems previously developed for the Italian.

Bi-directional LSTM-CNNs-CRF for Italian Sequence Labeling and Multi-Task Learning

Pierpaolo Basile;Pierluigi Cassotti;Lucia Siciliani;Giovanni Semeraro
2017

Abstract

In this paper, we propose a Deep Learning architecture for several Italian Natural language processing tasks based on a state of the art model that exploits both word- and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture provided state of the art performance in several sequence labeling tasks for the English language. We exploit the same approach for the Italian language and extend it for performing a multi-task learning involving PoS-tagging and sentiment analysis. Results show that the system is able to achieve state of the art performance in all the tasks and in some cases overcome the best systems previously developed for the Italian.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/228576
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