Publications

@inproceedings{spicciaWordPredictionMethodology2015,

title = {A Word Prediction Methodology for Automatic Sentence Completion},

author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato and Giorgio Vassallo},

editor = { Li T. Wang W. Kankanhalli M.S.},

doi = {10.1109/ICOSC.2015.7050813},

isbn = {978-1-4799-7935-6},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015},

pages = {240--243},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network language models. textcopyright 2015 IEEE.},

keywords = {Computational Linguistics, Language Model, Latent Semantic Analysis, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{spiccia_word_2015,

title = {A word prediction methodology for automatic sentence completion},

author = {Carmelo Spiccia and Agnese Augello and Giovanni Pilato and Giorgio Vassallo},

editor = {Wang W. Li T. Kankanhalli M.S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925584145&doi=10.1109%2fICOSC.2015.7050813&partnerID=40&md5=59167065372818b2084abd8d4de13a73},

doi = {10.1109/ICOSC.2015.7050813},

isbn = {978-1-4799-7935-6},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015},

pages = {240–243},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network language models. © 2015 IEEE.},

keywords = {Computational Linguistics, Language Model, Latent Semantic Analysis, Semantic Computing, Semantic Spaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gambinoVirtualConversationReal2008,

title = {Virtual Conversation with a Real Talking Head},

author = { Orazio Gambino and Agnese Augello and Alessandro Caronia and Giovanni Pilato and Roberto Pirrone and Salvatore Gaglio},

doi = {10.1109/HSI.2008.4581446},

isbn = {1-4244-1543-8 978-1-4244-1543-4},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 Conference on Human System Interaction, HSI 2008},

pages = {263--268},

abstract = {A talking head is system performing an animated face model synchronized with a speech synthesis module. It is used as a presentation layer of a conversational Agent which provide an answer . It provides an answer when a query is written as an input by the user. The textual answer is converted into facial movements of a 3D face model whose lips and tongue movements are synchronized with the sound of the synthetic voice. The Client-Server paradigm has been used for the WEB infrastructure delegating the animation and synchronization to the client, so that the server can satisfy multiple requests from clients; while the Chatbot, the Digital Signal Processing and the Natural language Processing are provided by the server. textcopyright 2008 IEEE.},

keywords = {3D Modelling, Animation, Artificial intelligence, Computational Linguistics, Conversational Agents, Human computer interaction, Natural Language Processing, Talking Heads},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gambino_virtual_2008,

title = {Virtual conversation with a real talking head},

author = {Orazio Gambino and Agnese Augello and Alessandro Caronia and Giovanni Pilato and Roberto Pirrone and Salvatore Gaglio},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52149107110&doi=10.1109%2fHSI.2008.4581446&partnerID=40&md5=2ecaaf7a50db7564a6fa4e5b1e1da73e},

doi = {10.1109/HSI.2008.4581446},

isbn = {1-4244-1543-8 978-1-4244-1543-4},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 Conference on Human System Interaction, HSI 2008},

pages = {263–268},

abstract = {A talking head is system performing an animated face model synchronized with a speech synthesis module. It is used as a presentation layer of a conversational Agent which provide an answer . It provides an answer when a query is written as an input by the user. The textual answer is converted into facial movements of a 3D face model whose lips and tongue movements are synchronized with the sound of the synthetic voice. The Client-Server paradigm has been used for the WEB infrastructure delegating the animation and synchronization to the client, so that the server can satisfy multiple requests from clients; while the Chatbot, the Digital Signal Processing and the Natural language Processing are provided by the server. ©2008 IEEE.},

keywords = {3D Modelling, Animation, Artificial intelligence, Computational Linguistics, Conversational Agents, Human computer interaction, Natural Language Processing, Talking Heads},

pubstate = {published},

tppubtype = {inproceedings}

}