*Monthly online ILFC Seminar: interactions between formal and computational linguistics* https://gdr-lift.loria.fr/monthy-online-ilfc-seminar/

GdR LIFT is happy to announce the three forthcoming sessions of the ILFC seminar on the interactions between formal and computational linguistics:

- 2022/12/14 16:00-17:00 UTC+1: *Guy Emerson* (University of Cambridge; 15:00-16:00 UTC+0) Title: *Learning meaning in a logically structured model: An introduction to Functional Distributional Semantics* Abstract: *The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words based on the contexts in which they are observed. The mainstream approach is to represent meanings as vectors (such as Word2Vec embeddings, or contextualised BERT embeddings). However, vectors do not provide a natural way to talk about basic concepts in logic and formal semantics, such as truth and reference. While there have been many attempts to extend vector space models to support such concepts, there does not seem to be a clear solution. In this talk, I will instead go back to fundamentals, questioning whether we should represent meaning as a vector I will present the framework of Functional Distributional Semantics, which makes a clear distinction between words and the entities they refer to. The meaning of a word is represented as a binary classifier over entities, identifying whether the word could refer to the entity – in formal semantic terms, whether the word is true of the entity. The structure of the model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. The same kind of model can also be applied to different kinds of data, including both grounded data such as labelled images (where entities are observed) and also text data (where entities are latent). I will discuss results on semantic evaluation datasets, indicating that the model can learn information not captured by vector space models like Word2Vec and BERT. I will conclude with an outlook for future work, including challenges and opportunities of joint learning from different data sources.* - 2023/01/18 17:00-18:00 UTC+1: *Carolyn Anderson* (Wellesley College; 11:00-12:00 UTC-5) Title: [TBA] Abstract: [TBA] - 2023/02/15: *Steven T. Piantadosi* (UC Berkeley) Title: [TBA] Abstract: [TBA]

The seminar is held on Zoom. To attend the seminar and get updates, please subscribe to our mailing list (we now only rarely communicate through other mailing lists): https://sympa.inria.fr/sympa/subscribe/seminaire_ilfc