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20212026
most citedDMDD: A Large-Scale Dataset for Dataset Mentions Detection

12 citations · 29 across the 52 of their papers we have counts for

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Showing 2023 · cs.CLShow all

10 papers · 2 filters

cs.CL2023

Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion

Smriti Singh, Cornelia Caragea, Junyi Jessy Li

Situations and events evoke emotions in humans, but to what extent do they inform the prediction of emotion detection models? This work investigates how well human-annotated emotio…

cs.CL2023★ 1 cited

CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

Henry Peng Zou, Yue Zhou, Cornelia Caragea +1

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…

cs.CL2023★ 1 cited

JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification

Henry Peng Zou, Cornelia Caragea

Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. However, existing approaches based on pseudo-labeling suff…

cs.CL2023★ 1 cited

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank

Henry Peng Zou, Yue Zhou, Weizhi Zhang +1

During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support. Emergency relief organi…

cs.CL2023★ 3 cited

Sarcasm Detection in a Disaster Context

Tiberiu Sosea, Junyi Jessy Li, Cornelia Caragea

During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about…

cs.CL2023

Unsupervised Extractive Summarization of Emotion Triggers

Tiberiu Sosea, Hongli Zhan, Junyi Jessy Li +1

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoin…

cs.CL2023

Data Augmentation for Low-Resource Keyphrase Generation

Krishna Garg, Jishnu Ray Chowdhury, Cornelia Caragea

Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale…

cs.CL2023★ 12 cited

DMDD: A Large-Scale Dataset for Dataset Mentions Detection

Huitong Pan, Qi Zhang, Eduard Dragut +2

The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportun…

cs.CL2023

Neural Keyphrase Generation: Analysis and Evaluation

Tuhin Kundu, Jishnu Ray Chowdhury, Cornelia Caragea

Keyphrase generation aims at generating topical phrases from a given text either by copying from the original text (present keyphrases) or by producing new keyphrases (absent keyph…

cs.CL2023★ 2 cited

Semantic Tokenizer for Enhanced Natural Language Processing

Sandeep Mehta, Darpan Shah, Ravindra Kulkarni +1

Traditionally, NLP performance improvement has been focused on improving models and increasing the number of model parameters. NLP vocabulary construction has remained focused on m…