Alessandro Moschitti is a professor of the Computer Science and Information Engineering Department of the Trento University. He took his PhD in Computer Science from the University of Rome "Tor Vergata" in 2003. He has worked as an associate researcher for the University of Texas at Dallas (for two years), as a visiting professor for the CCLS department of Columbia University and more recently as visiting researcher for the IBM Watson research center of New York for the Jeopardy! project and as visiting professor of the cognitive science and natural language processing (NLP) department of The University of Colorado at Boulder. His expertise concerns theoretical and applied machine learning (ML) in the areas of NLP, IR and Data Mining. He has devised innovative kernels within support vector and other kernel-based machines for advanced syntactic/semantic processing. These have been documented in more than 170 scientific articles, published in the major conferences of several research communities, e.g., ACL, ICML, ECML-PKDD, SIGIR, CIKM, and ICDM. He is also an active area chair and PC member for the conferences/journals of the disciplines above. He has participated in six projects of the European Community (EC) and in three US projects: MTBF with Con-Edison, IQAS for the ARDA AQUAINT PROGRAM and Deep QA (the Jeopardy! challenge) with IBM. Currently, he is the project consortium coordinator of the EC Coordinate Action, EternalS, project coordinator of PRIN 2008 and LiMoSINe EC project. He has received two IBM Faculty Awards, a Google Faculty Award and other prestigious best-paper awards.

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Research

The iKernels group led by Alessandro carries out advanced research on machine learning methods for syntactic and semantic processing of natural language. This aims at improving on the state-of-the-art in Information Search and Retrieval by enriching language models (typically based on bag-of-words) with structural representations and semantics.

In the context of natural language the group owns internationally recognized expertise in the following applications: Question Answering, FrameNet and PropBank Predicate Argument Extraction (Semantic Role Labeling), Relation Extraction, Syntactic and Semantic Parsing, Co-reference resolution, Text Categorization, Textual Entailment Recognition, Word Sense Disambiguation, Entity Recognition and Normalization, Opinion Mining, Speech and Noisy Text Processing, Text Similarity and Summarization.

Regarding Machine Learning, the group is developing new theory and methods for Kernel Machines: Kernel Methods, Structural Kernels, Support Vector Machines, On-line Learning, Structured Output Spaces, Multi-label and Hierarchical Classification and Re-Ranking.

Theory and methods are also applied to broader ICT areas than natural language processing and Information Retrieval, such as electronic feeder failure detection, bioinformatics, and automatic software processing (e.g., anomaly detection, code classification).