2 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods
Zhenhua Wang, Guang Xu, Ming Ren
With the ascent of large language models (LLM), natural language processing has witnessed enhancements, such as LLM-based data augmentation. Nonetheless, prior research harbors two…
Exploring the law of text geographic information
Zhenhua Wang, Daiyu Zhang, Ming Ren +1
Textual geographic information is indispensable and heavily relied upon in practical applications. The absence of clear distribution poses challenges in effectively harnessing geog…
Will sentiment analysis need subculture? A new data augmentation approach
Zhenhua Wang, Simin He, Guang Xu +1
Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human f…
Yes, DLGM! A novel hierarchical model for hazard classification
Zhenhua Wang, Ming Ren, Dong Gao +1
Hazards can be exposed by HAZOP as text information, and studying their classification is of great significance to the development of industrial informatics, which is conducive to…
A new hazard event classification model via deep learning and multifractal
Zhenhua Wang, Bin Wang, Ming Ren +1
Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and sug…
A Zipf's Law-based Text Generation Approach for Addressing Imbalance in Entity Extraction
Zhenhua Wang, Ming Ren, Dong Gao +1
Entity extraction is critical in the intelligent advancement across diverse domains. Nevertheless, a challenge to its effectiveness arises from the data imbalance. This paper propo…