Bi-directional LSTM-CNNs-CRF for Italian Sequence Labeling and Multi-Task Learning
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...
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| Xuất bản năm: | URI:https://journals.openedition.org/ijcol, |
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| Những tác giả chính: | , , , |
| Định dạng: | Article ou chapitre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
IJCoL
2020
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| Truy cập trực tuyến: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Tóm tắt: | 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. |
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