Segue convite seminário online na EMAp/FGV esta quinta-feira as 16h.

At.,
Alexandre


> Begin forwarded message:
> 
> From: Escola de Matematica Aplicada <e...@fgv.br>
> Subject: FGV EMAp | Seminário: "Learning meaning from text with a logically 
> structured model: An introduction to Functional Distributional Semantics" por 
> Guy Emerson
> Date: 16 August 2021 09:15:29 GMT-3
> 
>  
> 
> LEARNING MEANING FROM TEXT WITH A LOGICALLY STRUCTURED MODEL:
> AN INTRODUCTION TO FUNCTIONAL DISTRIBUTIONAL SEMANTICS
> 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).  When learning such a 
> classifier from a corpus of text, the challenge is that the entities 
> themselves are not observed, and so I define a probabilistic graphical model 
> where entities are latent variables.  This graphical model provides a natural 
> way to model logical inference, semantic composition, and context-dependent 
> meanings, where Bayesian inference plays a crucial role.  To make inference 
> tractable, I use amortised variational inference with a graph-convolutional 
> network.  The model is trained on WikiWoods, a parsed version of the English 
> Wikipedia.  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 joint learning from text, ontologies, and grounded data such 
> as images.
>  
> Texto informado pelo autor.
>  
> Quando e Onde:
> 19 de Agosto de 2021, às 16h
> Via Zoom
> Registre-se com antecedência para este seminário:
> https://fgv-br.zoom.us/meeting/register/tJcsceyupjIjHtK9BmjGS2MH7CpndEK6GGZc 
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> Após o registro, você receberá um e-mail de confirmação contendo informações 
> sobre conexão no seminário.
>  
> Palestrante:
> Guy Emerson - I began my undergraduate studies as a mathematician at Trinity 
> College, Cambridge, before switching to a masters in computer science, 
> focusing on computational linguistics.  I then spent one year studying at 
> Saarland University and working at DFKI (the German Research Centre for 
> Artificial Intelligence), before returning to Cambridge to pursue a PhD under 
> the supervision of Ann Copestake, which I completed in 2018.  My thesis was 
> awarded Honourable Mention (top 3) for the 2019 E.W. Beth Dissertation Prize, 
> and Highly Commended (top 3) for the 2019 CPHC/BCS Distinguished Dissertation 
> Award.  I am currently a Research Fellow at Gonville & Caius College, a 
> Departmental Early-Career Academic Fellow at the Department of Computer 
> Science and Technology, and an Executive Director of Cambridge Language 
> Sciences.  I also enjoy ballroom and latin dancing (which includes samba, but 
> the Europeanised version, not the Brazilian version!)
>  
>  
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