Obiettivo In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g. text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning. In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g. facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g. log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning. Campo scientifico natural sciencescomputer and information sciencesartificial intelligencemachine learningsupervised learningnatural sciencescomputer and information sciencesdata sciencenatural language processing Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-StG-2015 - ERC Starting Grant Invito a presentare proposte ERC-2015-STG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-STG - Starting Grant Istituzione ospitante THE UNIVERSITY OF EDINBURGH Contribution nette de l'UE € 1 289 942,50 Indirizzo OLD COLLEGE, SOUTH BRIDGE EH8 9YL Edinburgh Regno Unito Mostra sulla mappa Regione Scotland Eastern Scotland Edinburgh Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 289 942,50 Beneficiari (2) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto THE UNIVERSITY OF EDINBURGH Regno Unito Contribution nette de l'UE € 1 289 942,50 Indirizzo OLD COLLEGE, SOUTH BRIDGE EH8 9YL Edinburgh Mostra sulla mappa Regione Scotland Eastern Scotland Edinburgh Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 289 942,50 UNIVERSITEIT VAN AMSTERDAM Paesi Bassi Contribution nette de l'UE € 167 242,50 Indirizzo SPUI 21 1012WX Amsterdam Mostra sulla mappa Regione West-Nederland Noord-Holland Groot-Amsterdam Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 167 242,50
