Titlebook: Natural Language Processing and Information Systems; 27th International C Paolo Rosso,Valerio Basile,Farid Meziane Conference proceedings 2

嘲笑

Marcelo Fischer,Rejwanul Haque,Paul Stynes,Pramod Pathaklysis, probabilistic neural networks and multi-layer feed-forward neural networks. In addition, the book describes local and global optimization methods and their application to seismic reflection data. Given i978-3-030-45664-1978-3-030-45662-7Series ISSN 2364-9119 Series E-ISSN 2364-9127

培养

Natural Language Processing and Information Systems27th International C

窝转脊椎动物

TOXIN

CYN

On-Device Language Detection and Classification of Extreme Short Text from Calendar Titles Across La. Our language detection models with accuracies of 96%, outperform existing language detection tools by 20% and our event classifiers achieved 92%, 94%, 87% and 90% accuracies across, English, Korean and German, French respectively. Currently tested CEC module architecture delivers the fastest (4 ms

苦涩

Identifying Fake News in Brazilian Portuguese This paper investigates the application of BERT for fake news identification in Brazilian Portuguese. In addition to BERT, we also tested a number of widely-used machine learning (ML) algorithms, methods and strategies for this task. We found that fake news identification models built using advance

Perineum

e limited in scope, offers a laboratory analysis of the attenuation effects for sur­ face layers. The authors confirm that seismic attenuation in sedimentary la978-3-7643-6263-8978-3-0348-8415-0Series ISSN 2504-3625 Series E-ISSN 2504-3633

易受刺激

Convolutional Graph Neural Networks for Hate Speech Detection in Data-Poor Settingson that considers the similarity among nodes in the graph to improve the final classification. Particularly, our goal is to overcome hate speech detection in data-poor settings. As a result we found that our model is more stable than other state-of-the-art deep learning models with few data in the considered datasets.

BRIEF

KEGEL