RIW | Ep1 - Modeling and extracting complex information from natural language text

Replay of the webinar "Modeling and extracting complex information from natural language text" with Fabian Suchanek and Chloé Clavel, professors at Télécom Paris, Institut Polytechnique de Paris.

Find the upcoming webinars here: https://www.telecom-paris.fr/en/research/research-and-innovation-webinars

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Description

The NoRDF Project is a scientific project at Télécom Paris, Institut Polytechnique de Paris that aims to model and extract complex information from natural language text. More precisely, we want to enrich knowledge bases with events, causation, precedence, stories, negation, and beliefs. In particular, we will investigate the expression of sentiment. We want to extract this type of information at scale from structured and unstructured sources, and we want to allow machines to reason on it. The project brings together research on knowledge representation, on reasoning, and on information extraction, and aims to be useful for applications such as fake news detection, the modeling of controversies, or the analysis of the e-reputation of a company.

In the first part of the talk, we gave a general overview of the project. In the second part of the talk, we highlighted the challenges for sentiment analysis in human-human and human-agent interactions.


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00:00 - Introduction

00:54 - The NoRDF Project
Fabian Suchanek, Télécom Paris
23:06 - Focus on the extraction of social behaviors from texts within the NoRDF project
Chloé Clavel, Télécom Paris
43:23 - Questions/Answers