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Bayesian Methods for Unsupervised Multilingual Grammar Induction (séminaire du labex Digicosme du LIMSI - UPR 3251 CNRS, Universités UPMC et Paris-Sud) (2016)| shared_uid | 2571 |
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| title | Bayesian Methods for Unsupervised Multilingual Grammar Induction (séminaire du labex Digicosme du LIMSI - UPR 3251 CNRS, Universités UPMC et Paris-Sud) |
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| type | Séminaire |
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| year | 2016 |
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| start_date | 2017/06/06 |
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| stop_date | 2017/07/12 |
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| schedule | variable |
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| active | no |
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| website | https://www.limsi.fr/fr/actualites/672-seminaires-tim-miller |
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| summary | En juin et juillet, le LIMSI recevra Timothy Miller comme professeur invité Digicosme en Traitement Automatique de la Langue Naturelle. Vous trouverez ci-dessous le programme de la série de séminaires qui seront donnés par Tim lors de son séjour.
Timothy Miller will be visiting LIMSI this summer as a Digicosme invited professor in Natural Language Processing. Please find below the program of the seminar series that Tim will give during his stay. You are all welcome to attend and meet him.
Mini-bio: Timothy Miller, PhD, is a scientist at the Computational Health Informatics Program (CHIP) at Boston Children's Hospital and an Instructor at Harvard Medical School. His research background is in computer science, with his thesis (2010) describing linear time syntactic models for speech repair. In his current position, he works on a variety of clinical natural language processing problems. He has made core contributions in temporal information extraction (Lin et al, 2014, Miller et al, 2013, Miller et al., 2015), UMLS relation extraction (Dligach et al, 2013), coreference resolution (Miller et al, 2012, Zheng et al, 2012, Miller et al., 2017a), and negation detection (Wu et al, 2014, Miller et al., 2017b). He also is a primary contributer to open source projects, including Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) and ClearTK. He is currently interested in Bayesian grammar induction, temporal information extraction in the clinical domain, and domain adaptation for clinical NLP. |
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| responsibles | Névéol |
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