This is a template for authors who are preparing their contribution for one of CERN's official publications (CERN Yellow Report series, CERN Proceedings series, etc.). See https://publishing.cern.ch/ for further details.
Gesture controlled robot is a robot which can be controlled by simple gesture. The
user just needs to wear a gesture device which include a sensor. The sensor will record
the movement of hand in a specific direction which will result in the movement of the
robot in the respective direction. The robot and the gesture device are connected
wirelessly via radio waves. The wireless communication enables the user to interact
with the robot in a more friendly way.
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.