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English. In this paper, we propose a Deep Learning architecture for sequence labeling based on a state of the art model that exploits both wordand characterlevel representations through the combination of bidirectional LSTM, CNN and CRF. We evaluate the proposed method on three Natural Language Processing tasks for Italian: PoS-tagging of tweets, Named Entity Recognition and Super-Sense Tagging. 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. Italiano. In questo lavoro viene descritta un’architettura di Deep Learning per l’etichettatura di sequenze basata su un modello allo stato dell’arte che utilizza rappresentazioni sia a livello di carattere che di parola attraverso la combinazione di LSTM, CNN e CRF. Il metodo è stato valutato in tre task di elaborazione del linguaggio naturale per la lingua italiana: il PoS-tagging di tweet, il riconoscimento di entità e il Super-Sense Tagging. I risultati ottenuti dimostrano che il sistema è in grado di raggiungere prestazioni allo stato dell’arte in tutti i task e in alcuni casi riesce a superare i sistemi precedentemente sviluppati per la lingua italiana. 1 Background and Motivation Deep Learning (DL) gained a lot of attention in last years for its capacity to generalize models without the need of feature engineering and its ability to provide good performance. On the other hand good performance can be achieved by accurately designing the architecture used to perform the learning task. In Natural Language Processing (NLP) several DL architectures have been proposed to solve many tasks, ranging from speech recognition to parsing. Some typical NLP tasks can be solved as sequence labeling problem, such as part-of-speech (PoS) tagging and Named Entity Recognition (NER). Traditional high performance NLP methods for sequence labeling are linear statistical models, including Conditional Random Fields (CRF) and Hidden Markov Models (HMM) (Ratinov and Roth, 2009; Passos et al., 2014; Luo et al., 2015), which rely on hand-crafted features and task/language specific resources. However, developing such task/language specific resources has a cost, moreover it makes difficult to adapt the model to new tasks, new domains or new languages. In (Ma and Hovy, 2016), the authors propose a state of the art sequence labeling method based on a neural network architecture that benefits from both wordand character-level representations through the combination of bidirectional LSTM, CNN and CRF. The method is able to achieve state of the art performance in sequence labeling tasks for the English without the use of hand-crafted features. In this paper, we exploit the aforementioned architecture for solving three NLP tasks in Italian: PoS-tagging of tweets, NER and Super Sense Tagging (SST). Our research question is to prove the effectiveness of the DL architecture in a different language, in this case Italian, without using language specific features. The results of the evaluation prove that our approach is able to achieve state of the art performance and in some cases it is able to overcome the best systems developed for the Italian without the usage of specific language resources. The paper is structured as follows: Section 2 provides details about our methodology and summarizes the DL architecture proposed in (Ma and Hovy, 2016), while Section 3 shows the results of the evaluation. Final remarks are reported in Section 4.

Bi-directional LSTM-CNNs-CRF for Italian Sequence Labeling