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State-of-the-art generalisation research in NLP: a taxonomy and review
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 …
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
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 …
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
Current research concentrates on studying discussions on social media related to structural
failures to improve disaster response strategies. However, detecting social web posts …
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
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 …
with a predominant emphasis on English texts. Tackling ABSA in low-resource languages …
Cross-language plagiarism detection: methods, tools, and challenges: a systematic review
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 …
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
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 …
during disasters. These information might be available in multiple languages due to the …
Transformer-based multi-task learning for disaster tweet categorisation
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 …
people to post messages seeking help during crisis situations. These messages can …
Crisismatch: Semi-supervised few-shot learning for fine-grained disaster tweet classification
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 …
Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and …
Semi-supervised few-shot learning for fine-grained disaster tweet classification
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 …
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
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 …
consequences. Recently, many tweets about the COVID-19 pandemic have been shared …