| |
|
Title: |
Learning Verb Meaning with Hidden Grammars |
|
Address: |
Madame Paola Merlo
Centre Universitaire d'Informatique
Université de Genève
Battelle Bâtiment A
7, route de Drize
CH-1227 Carouge |
|
|
Project Duration: |
10/1/2006 - 6/30/2009 |
|
Amount Granted: |
CHF 101,549.00 |
|
|
Funding Instrument: |
Project Support: Independent Basic Research
|
Principal Applicant
Merlo Paola
Carouge
» Details
|
Research Institution
Département de Linguistique Faculté des lettres Université de Genève
Genève
» Details
|
University
University of Geneva
» Details
|
Primary Discipline(s)
Other languages and literature
|
|
|
weakly supervised learning, Bayesian inference, role labelling, hidden variable models, natural language processing, machine learning, corpus-based modelling
|
|
|
Recent developments in natural language understanding have focused on data-driven statistical techniques, which exploit the current large amounts of available text as a learning resource to learn the meaning of a sentence. Natural language understanding has immediate applications in question-answering and information extraction. For example, an automatic flight reservation system processing the sentence "I want to book a flight from Geneva to New York" will need to know that "from Geneva" is the origin of the flight and "to New York" is the destination. The applications are many-fold and extremely relevant, in particular question-answering for dialogue systems and information extraction for biological data. Technically, the solution of this problems requires us to be able to assign semantic roles (origin, destination, in the given
example) automatically to any kind of text. We propose a method that is based on automatically inferring a hidden grammatical structure, which is the unobserved cause of the meaning of the sentence. We model therefore the problem as a problem of inferring hidden causes of observed events in uncertain environments. These methods hold promise for both the accuracy and the coverage of the results.
|
|
|
|