Artificial intelligence, Natural language processing, Machine learning, Support vector machine and Tree kernel are his primary areas of study. Within one scientific family, Alessandro Moschitti focuses on topics pertaining to Pattern recognition under Artificial intelligence, and may sometimes address concerns connected to Dependency. The various areas that Alessandro Moschitti examines in his Natural language processing study include Feature engineering, Semantics and Information retrieval.
His Machine learning research includes themes of Similarity measure, Sequence labeling and Machine translation. His Support vector machine research is multidisciplinary, incorporating perspectives in Kernel, Parse tree, Automatic learning, Predicate and Kernel. His study in Tree kernel is interdisciplinary in nature, drawing from both String kernel, Semantic similarity and Language identification.
Alessandro Moschitti mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Question answering and Tree kernel. His biological study deals with issues like Information retrieval, which deal with fields such as Set. His Natural language processing research incorporates elements of Semantics and Selection.
His Machine learning research is multidisciplinary, relying on both Relationship extraction and Classifier. His Question answering research is multidisciplinary, incorporating elements of Learning to rank, Artificial neural network, Inference, SemEval and Arabic. His studies in Tree kernel integrate themes in fields like Tree, Parse tree and Feature vector.
Alessandro Moschitti mostly deals with Artificial intelligence, Question answering, Natural language processing, Machine learning and Sentence. Transformer, Artificial neural network, Tree kernel, SemEval and Similarity are the subjects of his Artificial intelligence studies. His Question answering study incorporates themes from Arabic, Dialog box, Natural language and Data science.
The study incorporates disciplines such as Context, Training set, Space, Pairwise comparison and Selection in addition to Natural language processing. His Machine learning research incorporates themes from Multi-task learning and Inference. The concepts of his Sentence study are interwoven with issues in Ranking, State, Selection and Pattern recognition.
His primary areas of study are Artificial intelligence, Question answering, Natural language processing, Machine learning and Sentence. His Artificial intelligence research includes elements of Domain and Pattern recognition. His research integrates issues of Semantics and Kernel in his study of Natural language processing.
His Machine learning study integrates concerns from other disciplines, such as Relationship extraction, Crowdsourcing and Inference. His SemEval study also includes fields such as
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks
Aliaksei Severyn;Alessandro Moschitti.
international acm sigir conference on research and development in information retrieval (2015)
Twitter Sentiment Analysis with Deep Convolutional Neural Networks
Aliaksei Severyn;Alessandro Moschitti.
international acm sigir conference on research and development in information retrieval (2015)
CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes
Sameer Pradhan;Alessandro Moschitti;Nianwen Xue;Olga Uryupina.
empirical methods in natural language processing (2012)
Efficient convolution kernels for dependency and constituent syntactic trees
Alessandro Moschitti.
european conference on machine learning (2006)
Making Tree Kernels Practical for Natural Language Learning.
Alessandro Moschitti.
conference of the european chapter of the association for computational linguistics (2006)
A Study on Convolution Kernels for Shallow Statistic Parsing
Alessandro Moschitti.
meeting of the association for computational linguistics (2004)
Complex Linguistic Features for Text Classification: A Comprehensive Study
Alessandro Moschitti;Roberto Basili.
european conference on information retrieval (2004)
SemEval-2017 Task 3: Community Question Answering
Preslav Nakov;Doris Hoogeveen;Lluís Màrquez;Alessandro Moschitti.
meeting of the association for computational linguistics (2017)
Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification
Alessandro Moschitti;Silvia Quarteroni;Roberto Basili;Suresh Manandhar.
meeting of the association for computational linguistics (2007)
BART: A Modular Toolkit for Coreference Resolution
Yannick Versley;Simone Paolo Ponzetto;Massimo Poesio;Vladimir Eidelman.
meeting of the association for computational linguistics (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
University of Rome Tor Vergata
Qatar Computing Research Institute
Amazon (United States)
University of Trento
University of Bologna
Salesforce (United States)
MIT
IT University of Copenhagen
Queen Mary University of London
Fondazione Bruno Kessler
Royal Institute of Technology
University of Toronto
Tsinghua University
Indiana University
University College London
University of Maryland, Baltimore
Kansas State University
Tufts University
University of Hong Kong
Auburn University
Rensselaer Polytechnic Institute
Grenoble Alpes University
University of Bern
Pennsylvania State University
Memorial Sloan Kettering Cancer Center
University of Oxford