From june to july 2017, he goes to work with LIMSI's team on "Traitement Automatique de la Langue Naturelle".
Program :
Bayesian Methods for Unsupervised Multilingual Grammar Induction.- Séminaire GT TSDT 6/06 - 14h00 - LIMSI bat 508
- Introduction to sequence models for Natural Language Processing
- Sequence models for NLP – Focus on hidden Markov models (HMMs), including inference techniques, which form the core of the method, and their more complex siblings the hierarchical HMMs (HHMMs).
- Optimizing HMM inference with modern GPU hardware
- Alternative sequence models, including CRFs and RNNs
- Séminaire TLP 13/06 - 11h30 LIMSI bat 508
- Linear time parsing with HHMMs
- Parsing strategies – An introduction to bottom-up, top-down, and right-corner parsing, from a psycholinguistic perspective.
- Linear time parsing with HHMMs in a supervised machine learning framework
- Séminaire CEA-LIST 22/06 - 10h00 CEA, amphi 34 bat 862
- Bayesian inference for unsupervised POS tagging and parsing
- Bayesian inference for unsupervised POS tagging with HMMs, and unsupervised parsing with HHMMs
- Séminaire ILES 4/07 - 14h00 LIMSI bat 508
- Generalizability and domain adaptation in clinical NLP
- Overview of pipeline approaches to clinical NLP
- Evidence from multiple tasks that performance degrades across tasks
- Introduction to unsupervised domain adaptation algorithms
- Preliminary work on domain adaptation for negation extraction
- Séminaire GT D2K 12/07 - 9h00 LRI (bât PCRI) salle 455
- Coreference resolution: state of the art and application to biomedical text.
- Problem description, early systems, and applications – What is coreference, why is it important, what are some of the early methods, and what are some important use cases that rely on solving the coreference resolution problem?
- Machine learning approaches – An overview of common machine learning approaches for the task, including pairwise, mention-synchronous, agglomerative clustering, easy-first, and even some of the unsupervised approaches
- Biomedical coreference resolution – Domain-specific issues with solving coreference, as well as an introduction to domain-specific resources that are available for the task.
- Future directions for coreference research - An introduction to hot topics in coreference resolution, including search-based learning, neural-network based representation learning, and cross-document coreference, with suggestions for how these methods can be applied to biomedical texts.
Futur :
Develop a long-term collaboration with the Harvard Medical School team.Seules les personnes avec un identifiant peuvent lire le rapport de visite.