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20202025
most citedSentiment Analysis in the Era of Large Language Models: A Reality Check

56 citations · 106 across the 15 of their papers we have counts for

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13 papers · 1 filter

cs.CL20234 cited

Reasoning Implicit Sentiment with Chain-of-Thought Prompting

Hao Fei, Bobo Li, Qian Liu +3

While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA…

cs.CL20232 cited

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

Ran Zhou, Xin Li, Lidong Bing +2

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub…

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.CL2023

mPMR: A Multilingual Pre-trained Machine Reader at Scale

Weiwen Xu, Xin Li, Wai Lam +1

We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual p…