most citedSentiment Analysis in the Era of Large Language Models: A Reality Check

56 citations · 62 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2023

AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach

Jia Guo, Liying Cheng, Wenxuan Zhang +3

Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, e…

cs.CL20232 cited

Zero-Shot Text Classification via Self-Supervised Tuning

Chaoqun Liu, Wenxuan Zhang, Guizhen Chen +4

Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scal…

cs.CL202356 cited

Sentiment Analysis in the Era of Large Language Models: A Reality Check

Wenxuan Zhang, Yue Deng, Bing Liu +2

Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen consi…

cs.CL20234 cited

Enhancing Few-shot NER with Prompt Ordering based Data Augmentation

Huiming Wang, Liying Cheng, Wenxuan Zhang +2

Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recogniti…

cs.CL2023

Easy-to-Hard Learning for Information Extraction

Chang Gao, Wenxuan Zhang, Wai Lam +1

Information extraction (IE) systems aim to automatically extract structured information, such as named entities, relations between entities, and events, from unstructured texts. Wh…

cs.CL2023

Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis

Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1

Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.…