Text analysis, via natural language processing (NLP) methods, aims at high-level information extraction. Applications vary from document classification to automatic answering machine. 

Our works aims at the development of “intelligent” human-machine interfaces using probabilistic models applied to text. We are currently collaborating with the company ELTEIDE/MAZER for the development of “Laila, a chatbot with a human touch”.

 

Word2Vec Networks

A Word2Vec network produces a mapping of large word dictionary of words into points in a multi-dimensional space. Word2Vect are used in a large number of applications, that range from document classification to question answering. The mapping is learned in an unsupervised way and leads to representations that reflect also a “closeness” in meaning among the words. Algebraic manipulations can answer analogy questions such as

King + Man – Queen = Woman

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We have developed one of the most efficient Word2Vec systems for the Italian language and proposed a new algorithm for analogy-efficient representations.

  • Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) "An Analysis of Word2Vec for the Italian Language", In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_13 - Print ISBN: 978-981-15-5092-8 - Paper presented also at the Italian Workshop on Neural Networks WIRN 2019, June 12-14, 2019, Vietri s.m., Italy. [Preliminary Version on Arxiv]
  • G. Di Gennaro, A. Buonanno, F.A.N. Palmieri, "paper in preparation,"...

 

Intent Classification 

In a text dialog with a machine, questions have to be interpreted and classified. This is referred to as “intent classification”. We have proposed a neural network architecture based on LSTM (Log Short Term Memory).

 

  • Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N. (2021) Intent Classification in Question-Answering Using LSTM Architectures. In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_11  - Print ISBN: 978-981-15-5092-8 - Paper presented also at the Italian Workshop on Neural Networks WIRN 2019, June 12-14, 2019, Vietri s.m., Italy. [Preliminary Version on Arxiv]