State-of-the-art generalisation research in NLP: a taxonomy and review

D Hupkes, M Giulianelli, V Dankers, M Artetxe… - arxiv preprint arxiv …, 2022 - arxiv.org
The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

A semantic and syntactic enhanced neural model for financial sentiment analysis

C **ang, J Zhang, F Li, H Fei, D Ji - Information Processing & Management, 2022 - Elsevier
This paper studies the methodology of inferring bullish or bearish sentiments in the financial
domain. The task aims to predict a real value to represent the sentiment intensity concerning …

Infrastructure ombudsman: Mining future failure concerns from structural disaster response

MTA Chowdhury, S Datta, N Sharma… - Proceedings of the …, 2024 - dl.acm.org
Current research concentrates on studying discussions on social media related to structural
failures to improve disaster response strategies. However, detecting social web posts …

Addressing class-imbalance challenges in cross-lingual aspect-based sentiment analysis: Dynamic weighted loss and anti-decoupling

N Lin, M Zeng, X Liao, W Liu, A Yang, D Zhou - Expert Systems with …, 2024 - Elsevier
Numerous attempts have been made to address Aspect-based Sentiment Analysis (ABSA),
with a predominant emphasis on English texts. Tackling ABSA in low-resource languages …

Cross-language plagiarism detection: methods, tools, and challenges: a systematic review

MAB Tobar, MGJ Van Den Brand… - International Journal on …, 2022 - research.tue.nl
Plagiarism is one of the most serious academic offenses. However, people have adopted
different approaches to avoid plagiarism, such as transcribing excerpts from one language …

GNoM: graph neural network enhanced language models for disaster related multilingual text classification

S Ghosh, S Maji, MS Desarkar - Proceedings of the 14th ACM Web …, 2022 - dl.acm.org
Online social media works as a source of various valuable and actionable information
during disasters. These information might be available in multiple languages due to the …

Transformer-based multi-task learning for disaster tweet categorisation

C Wang, P Nulty, D Lillis - arxiv preprint arxiv:2110.08010, 2021 - arxiv.org
Social media has enabled people to circulate information in a timely fashion, thus motivating
people to post messages seeking help during crisis situations. These messages can …

Crisismatch: Semi-supervised few-shot learning for fine-grained disaster tweet classification

HP Zou, Y Zhou, C Caragea, D Caragea - arxiv preprint arxiv:2310.14627, 2023 - arxiv.org
The shared real-time information about natural disasters on social media platforms like
Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and …

Semi-supervised few-shot learning for fine-grained disaster tweet classification

HP Zou, C Caragea, Y Zhou, D Caragea - Proceedings of the 20th …, 2023 - par.nsf.gov
The shared real-time information about natural disasters on social media platforms like
Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and …

Identifying informative tweets during a pandemic via a topic-aware neural language model

W Gao, L Li, X Tao, J Zhou, J Tao - World wide web, 2023 - Springer
Every epidemic affects the real lives of many people around the world and leads to terrible
consequences. Recently, many tweets about the COVID-19 pandemic have been shared …