Fabio Massimo Zanzotto
Università degli Studi di Roma “Tor Vergata”
Dipartimento di Ingegneria dell’Impresa “Mario Lucertini”

Fabio Massimo Zanzotto is an Associate Professor at Department of Enterprise Engineering of the University of Rome “Tor Vergata”. Since 1998, he has interests in the research endeavor of Artificial Intelligence. He is active in the area of Natural Language Processing, mainly working in three areas: recognizing textual entailment, syntactic parsing for Italian, and, recently, distributed/distributional models for NLP. The term recognizing textual entailment (RTE) has been recently introduced in NLP to systematically foster studies in defining computational models that replicate the human ability to determine whether or not texts imply sentences. A simple RTE example is determining whether or not “Acme bought BigT” entails “Acme owns BigT”. Zanzotto has been interested in this scientific endeavor since its beginning in 2005. He is mainly interested in studying the application of machine learning models to the RTE. After 2005, a first exploratory year , during the investigation of the application of ML models to RTE task, Zanzotto produced a major innovation for the specific RTE field as well as for the related fields of natural language processing, machine learning, and graph analysis applied to text. It is still under analysis if the innovation can generate an interest in the research area of graph theory as it involves graph isomorphism on a particular class of graphs. The innovation is the following. In the context of machine learning models based on kernel functions, he proposed a novel class of feature spaces encoding first-order rewrite rules. With this new class of feature spaces and the related kernel functions, Zanzotto and his colleagues produced a system which scored at the 3rd place in the 2006 worldwide RTE Challenge (scored 1st among the academic systems) and in the 5th place in 2007 RTE Challenge. The exploration of full potential of this ides is still an on-going research as the class of first-order rewrite rule feature spaces can be applied in many areas of natural language processing (machine translation, document summarization, stylistic control systems, question-answering, and dialogue models) as well as in many other research areas. For these interests, Zanzotto has been invited to the program committees of all the textual entailment recognition workshops and challenges after the first. In 2009, Zanzotto co-chaired the ACL workshop of Applied Textual Inference that has a program committee with researchers in the area of textual entailment recognition and natural language processing. In 2009, he co-organized the Italian chapter of the RTE challenge in Evalita 2009. Zanzotto, together with Ido Dagan and Dan Roth, gave a tutorial titled “Textual Entailment Recognition” in the 45th Association of Computational Linguistics (ACL) Annual Conference. The tutorial has originated a book that is in preparation. He co-organized two editions of the TextGraphs Workshop series (2010,2011). He steadly participated to the program committees of the TextGraphs (2007, 2008, 2012) and he partecipated to the program committee of the workshop MLG-2010: Mining and Learning from Graphs.
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