Dies ist unsere neueste Auswahl an weltweiten Veröffentlichungen und Patenten in englischer Sprache zum Thema Natural Language Processing (NLP), die in zahlreichen wissenschaftlichen Online-Zeitschriften zu den Themen natürliche Sprachverarbeitung, Tokenisierung, Stemming, Lemmatisierung, Part-of-Speech, Named Entity Recognition und Sentiment Analysis veröffentlicht wurden.
Patente: nicht aktuell Patent zu diesem speziellen Thema. Versuchen Sie es bitte mit der umfangreichen manuellen Suche in der oben verlinkten Patentdatenbank.
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
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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
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