| Autor: |
Pierpaolo Basile, Pierluigi Cassotti, Lucia Siciliani, Giovanni Semeraro |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
IJCoL, Vol 3, Iss 2, Pp 37-50 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2499-4553 |
| DOI: |
10.4000/ijcol.553 |
| Popis: |
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 overcomes the best systems previously developed for the Italian. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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