Il s'agit de notre dernière sélection de publications et de brevets mondiaux en anglais sur le traitement du langage naturel (NLP), parmi de nombreuses revues scientifiques en ligne, classées et axées sur le traitement du langage naturel, la tokenisation, le stemming, la lemmatisation, la partie de discours, la reconnaissance des entités nommées et l'analyse des sentiments.
Brevets : pas d'actualité brevet sur ce sujet particulier. Veuillez essayer la recherche manuelle approfondie dans la base de données des brevets dont le lien figure juste au-dessus.
regulatory challenges, technical solutions, and practical pathways
Published on 2025-02-19 by @OXFORD
Abstract: AbstractThis paper thoroughly explores the complex interplay between blockchain technology and the General Data Protection Regulation (GDPR) of the European Union, alongside the substantial challenges and potential opportunities stemming from their interaction. While the challenges of decentralization and immutability in blockchain are well-documented, this paper advances the discussion by incorporating legal developments, such as evolving interpretations of joint controllership and new advisory[...]
Our summary: This paper examines the regulatory challenges and technical solutions in aligning blockchain technology with GDPR principles, proposing practical pathways for compliance through innovative solutions such as chameleon hashes and zero-knowledge proofs.
blockchain technology, General Data Protection Regulation (GDPR), compliance challenges, innovative solutions
Publication
Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis
Published on 2024-02-03 by Shiman Zhao, Yutao Xie, Wei Chen, Tengjiao Wang, Jiahui Yao, Jiabin Zheng @MIT
Abstract: Few-shot Aspect Category Sentiment Analysis (ACSA) is a crucial task for aspect-based sentiment analysis, which aims to detect sentiment polarity for a given aspect category in a sentence with limited data. However, few-shot learning methods focus on distance metrics between the query and support sets to classify queries, heavily relying on aspect distributions in the embedding space. Thus, they suffer from overlapping distributions of aspect embeddings caused by irrelevant sentiment noise among[...]
Our summary: Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis. Crucial task for aspect-based sentiment analysis. Proposes metric-free method using Dual Relations Propagation. Achieves improvement in accuracy and F1 score.
learning, network, relations, propagation, sentiment
Publication
