Paris NLP Season 3 Meetup #5

Seating is on a first come, first served basis whether you have RSVPed or not, so we suggest arriving early. We can host 70 people.

La salle permet d'accueillir 70 personnes. L'inscription est obligatoire mais ne garantit pas que vous pourrez entrer, nous vous recommandons donc d'arriver un peu en avance.

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Talks:

• [In English] Alexis Dutot, Linkfluence

At Linkfluence, we analyze millions of social media posts per day in more than 60 languages. This represents thousands of noisy user-generated documents per second passing through our internal enrichment pipeline. This volume combined with the real-time constraint prevents us from using cross lingual BERT-like models.

In this talk we will focus on multilingual sentiment analysis and emotion detection tasks based on social media data. Only a few annotated corpora tackle these tasks and the vast majority of them is dedicated to the English language. We will see how we fully exploit the potential of emojis as a universal expression of sentiment and emotion in order to build accurate sentiment analysis and emotion detection “real-time” deep learning systems in several languages using solely English annotated corpora.

• [In English] Benoît Lebreton, Sacha Samama and Tom Stringer, Quantmetry

Melusine (https://github.com/MAIF/melusine) is an open source library developed by Quantmetry and MAIF. The talk focuses on technical issues raised by Melusine’s open source implementation, as well as underlying neural models and algorithms that are being leveraged.

• [Canceled] [In English] Janna Lipenkova, Anacode.de

Automatic ontology construction from lexical relations

Applications in data and text analytics often have an ontology as their conceptual backbone - that is, a hierarchical representation of the underlying knowledge domain. However, such representations are tedious to construct, maintain and customize in a manual fashion. In this talk, I will show how text data and lexical relations such as hypernymy, synonymy and meronymy can be leveraged to automatically construct ontologies. After a review of different unsupervised and distant-supervised methods proposed for lexical relation extraction from text, I will explain Anacode's approach to building and maintaining large-scale, multilingual ontologies for the domain of business and market intelligence.