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Found 8 papers, 2 papers with code
Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis
In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like ROUGE and BLEU.
The Radiation Oncology NLP Database
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease.
Differentiate ChatGPT-generated and Human-written Medical Texts
We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks
In this paper, we propose a new joint model with a wheel-graph attention network (Wheel-GAT) which is able to model interrelated connections directly for intent detection and slot filling.
