Publications associées à Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...

ASSOC COMPUTING MACHINERY2023

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Lloyd Haydn Hughes

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

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Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

Dense Multitask Learning to Reconfigure Comics

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In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comic panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narrativ ...

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Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents

Jérôme Baudry, Nicolas Christophe Chachereau, Bhargav Srinivasa Desikan, Prakhar Gupta

Patents have traditionally been used in the history of technology as an indication of the thinking process of the inventors, of the challenges or “reverse salients” they faced, or of the social groups influencing the construction of technology. More recent ...

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Strong Heuristics for Named Entity Linking

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Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods. However, such methods tend to come with caveats, such as no integration of ...

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Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

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Low-Rank Subspaces for Unsupervised Entity Linking

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Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

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Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Dense Multitask Learning to Reconfigure Comics

Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents

Strong Heuristics for Named Entity Linking

Learning computationally efficient static word and sentence representations

Further results on latent discourse models and word embeddings

Lightweight Cross-Lingual Sentence Representation Learning

Low-Rank Subspaces for Unsupervised Entity Linking

Graph Chatbot

Chattez avec Graph Search

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

Further results on latent discourse models and word embeddings

Dense Multitask Learning to Reconfigure Comics

Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents

Learning computationally efficient static word and sentence representations

Low-Rank Subspaces for Unsupervised Entity Linking

Strong Heuristics for Named Entity Linking

Lightweight Cross-Lingual Sentence Representation Learning