Researcher at Inria, head of the ALPAGE (2014-2016) and ALMAnaCH (2017-) teams. Co-founder of the Verbatim Analysis (2009-) and opensquare (2016-) Inria start-ups.
Topics of interest
Computational linguistics, Natural Language Processing (NLP), NLP applications.
Project in Prairie
Benoît Sagot will focus on improving and better understanding neural approaches to NLP and integrating linguistic and extra-linguistic contextual information. He will study how non-neural approaches and language resources can contribute to improving neural NLP systems in low-resource and non-edited scenarios. Applications, both academic and industrial, will include computational linguistics and sociolinguistics, opinion mining in survey results, NLP for financial and historical documents, and text simplification to help people with disabilities.
Most current research in NLP focuses on neural architectures that rely on
large volumes of data, in the form of both raw text and costly annotated corpora. The increasing amount of data necessary to train such models is not available for all languages and can require massive computational resources. Moreover, these approaches are highly sensitive to language variation, illustrated for instance by domain-specific texts, historical documents and non-edited content as found on social media. To address these issues and allow for a wider deployment of NLP technologies, this bottleneck must be overcome. This will require new models that better exploit the complex structure of language and the context in which it is used.