Alessandro Moschitti is an Associate Professor at of the Department of Communication and Information Technology of the University of Trento. He is currently a Principal Applied Research Scientist at Amazon.
He graduated in 1998 from the University of Rome "La Sapienza" with a Master Degree in Computer Science and obtained his PhD in Computer Science at the University of Rome "Tor Vergata" in 2003. He has worked as (i) a research fellow for the University of Texas at Dallas, (ii) a visiting professor at the University of Columbia (NY), Colorado and John Hopkins, (iii) a visiting researcher at the IBM Watson Research center (participating at the Jeopardy! Challenge) and at MIT-CSAIL, (iv) a Principal Research Scientist at the Qatar Computing Research Institute (QCRI), within the Hamad Bin Khalifa University.
His expertise concerns theoretical and applied machine learning (ML) in the areas of Natural Language Processing (NLP), Information Retrieval (IR) and Data Mining. He has devised innovative structural kernels and neural networks for advanced syntactic/semantic processing, documented by more than 260 scientific articles published in NLP, IR and ML communities. He has been the General Chair of EMNLP 2014, a PC co-chair of CoNLL 2015, an action editor of TACL, and on the editorial board of MLJ, JAIR and JNLE. He has received four IBM Faculty awards, one Google Faculty award, five best paper awards and the best researcher award from Trento University. He has lead many projects, currently is the PI (QCRI side) of a large collaboration project between MIT CSAIL and QCRI.
Machine Learning for Natural Language Processing and Information Retrieval Natural Language Processing Applications: FrameNet and PropBank predicate argument extraction (Semantic Role Labeling), Relation Extraction, Co-reference resolution, Text Categorization, Textual Entailments, Question Answering, Word Sense Disambiguation, Named Entity Recognition, Named Entity Disambiguation, Spoken Dialog Systems and Text Summarization.
Kernel Methods, Support Vector Machines and on-line Learning: Tree Kernel Engineering, Fast and Effective Tree Kernels, Kernels for Re-Ranking, Kernels for Bioinformatics, Fast Kernels over Tree Sets, Syntactic/Semantic Tree Kernels, Shallow Semantic Tree Kernels, Lexical Semantic Similarity Kernels.
Studying and design of models combining innovative syntactic/semantic text representations and novel learning algorithms for the design of Information Retrieval systems. Such models may consider graph structures of events to compute both the answer to complex questions and its explanation in terms of graph dependency/implications.
Novel algorithms that both (a) easily learn Natural Language phenomena and other complex problems and (b) provide an explanation of the learnt model.