In this study, we propose the use of Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) to generate synthetic data for crop recommendation (CR). CR …

Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data
even if under zero-shot setting. Recent studies have shown that large language models …

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific
sentiment polarities toward certain aspects of products or services behind the social media …

Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information
systems that aims to simultaneously extract entities with semantic relations from a document …

Large language models have unlocked strong multi-task capabilities from reading instructive
prompts. However, recent studies have shown that existing large models still have difficulty …

Current generative knowledge graph construction approaches usually fail to capture
structural knowledge by simply flattening natural language into serialized texts or a …

Persona attribute extraction is critical for personalized human-computer interaction.
Dialogue is an important medium that communicates and delivers persona information …

We propose a new strategy for applying large pre-trained language models to novel tasks
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …