About
I am a PhD student in the Computer
Science department at University of Geneva.
My interests are in natural language processing, machine learning and computational complexity theory.
My advisors are James Henderson at
University of Edinburgh and Christian Pellegrini at University of Geneva.
February 2008: I moved to the Cognitive Computation Group at the University of Illinois at Urbana-Champaign.
You can contact me at titov (at) uiuc.edu
[Download CV]
Refereed Publications
2008
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A Joint Model of Text and Aspect Ratings for Sentiment Summarization
Ivan Titov, Ryan McDonald
46th Meeting of Association for Computational Linguistics (ACL-08). Columbus, OH, USA, 2008.
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Modeling Online Reviews with Multi-Grain Topic Models
Ivan Titov, Ryan McDonald
17th International World Wide Web Conference (WWW-2008). Beijing, China, 2008.
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A Latent Variable Model for Generative Dependency Parsing
Ivan Titov, James Henderson
To Appear in H. Bunt, P. Merlo and J. Nivre, editors, Trends in Parsing Technology, Text, Speech and Language Technology Series (Kluwer), 2008.
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A Latent Variable Model of Synchronous Parsing for Syntactic and Semantic Dependencies
James Henderson, Paola Merlo, Gabriele Musillo, Ivan Titov
CoNLL 2008 Shared Task., Conf. on Computational Natural Language Learning (CoNLL-08), Manchester, UK, 2008.
2007
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Constituent Parsing with Incremental Sigmoid Belief Networks
Ivan Titov, James Henderson
45th Meeting of Association for Computational Linguistics (ACL-07). Prague, Czech Republic, 2007.
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Incremental Bayesian Networks for Structure Prediction
Ivan Titov, James Henderson
24th International Conference on Machine Learning (ICML-07). Corvallis, OR, USA, 2007.
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Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
Ivan Titov, James Henderson
CoNLL 2007 Shared Task. Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-07), Prague, Czech Republic, 2007. (3rd result out of 23)
[Download parser]
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A Latent Variable Model for Generative Dependency Parsing
Ivan Titov, James Henderson
International Conference on Parsing Technologies (IWPT-07). Prague, Czech Republic, 2007.
[Download parser]
2006
2005
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Data-Defined Kernels for Parse Reranking Derived from Probabilistic Models
James Henderson, Ivan Titov
43rd Meeting of Association for Computational Linguisticsi (ACL-05). Ann Arbor, USA, 2005
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Deriving Kernels from MLP Probability Estimators for Large Categorization Problems
Ivan Titov, James Henderson
International Joint Conference on Neural Networks (IJCNN-05). Montreal, Canada, 2005
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Large Margin Multiple Hyperplane Classification for Content-Based Multimedia Retrieval
Serhiy Kosinov, Ivan Titov, Stephane Marchand-Maillet
International Conf. on Machine Learning (ICML-05), Workshop on Machine Learning Techniques for Processing Multimedia Content. Bonn, Germany, 2005
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Parsing with Kernels Induced from Probabilistic Models
Ivan Titov, James Henderson
Conference on Computational Linguistics and Intelligent Technologies (Dialog-05). Moscow, Russia (in Russian), 2005
Other Papers
Research Projects
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Neural Networks for Structure Processing Applied to Broad Coverage Natural Language Parsing (finished: Oct, 2005)
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Kernel-Based Structure Processing for Natural Language Parsing (Nov, 2005 - ...)
Education
Master Thesis
Complexity and Approximation of Optimal Communication Cost Spanning Tree Problems, 2003
Under supervision of Nikolay N. Vasiliev, St. Petersburg Department of Steklov Institute of Mathematics and St. Petersburg State Polytechnical University).
Software
ISBN Dependency Parser
New! ISBN parser described in [Titov and Henderson, IWPT 2007] and evaluated in [Titov and Henderson, EMNLP-CoNLL 2007].
[Download it here]
ISBN Constituent Parser
ISBN parser described in [Titov and Henderson, ACL 2007, Titov and Henderson, ICML 2007]
will be available for download very soon.
SSN Parsing Package
This package implements a statistical syntactic parser for natural language sentences. It estimates the probability of a parse using a neural network architecture for structure processing called Simple Synchrony Networks.
This package includes two trained parsers which can be used to parse English text. It also supports training new generative models or new conditionally-trained models. For a description of the generative SSN parsing model, see [Henderson, NAACL 2003]. For a description of conditional training with this model see [Henderson, ACL 2004].
A future version of this package will include the code to support deriving data-defined kernels from SSNs [Henderson and Titov, ACL 2005], [Titov and Henderson, IJCNN 2005].
This software was originally written by James Henderson and modified substantially by Ivan Titov.
[Download it here]
Teaching
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Pattern Recognition
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Algorithms for Learning and Optimization
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