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Overleaf and the National Academy of Sciences (NAS)
This is a presentation I gave about what LaTeX can do some years ago. I've continued to update it (slightly) for other talks. As this is quite a large project with many files and packages, users on the Free plan may not be able to compile it successfully on Overleaf. If that's the case, feel free to grab the PDF here, and download the sources as a .zip for offline perusal/compilation!
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.