Linguistic Analysis of Verb Tense Usage Patterns in Computer Science Paper Abstracts

Minghui Wang; Lichao Zhu

doi:10.69987/JACS.2023.30603

Authors

Minghui Wang School of Software and Microelectronics, Peking University, Beijing, China Author
Lichao Zhu Management science and engineering, Beijing Institute of Technology, Beijing, China Author

DOI:

https://doi.org/10.69987/JACS.2023.30603

Keywords:

corpus linguistics, verb tense analysis, academic writing, computational linguistics, natural language processing

Abstract

This study presents a comprehensive corpus-based analysis of verb tense usage patterns in computer science paper abstracts, examining a dataset of 15,000 abstracts from major IEEE and ACM conferences published between 2019-2024. Natural language processing techniques combined with manual linguistic annotation reveal distinct tense distribution patterns that correlate with rhetorical functions and disciplinary conventions. Statistical analysis demonstrates that simple present tense dominates at 42.3% frequency, followed by simple past (31.7%) and present perfect (18.2%). Machine learning classification achieves 89.4% accuracy in predicting tense categories using contextual features. Cross-sectional analysis reveals significant variation across computer science subfields, with systems papers exhibiting higher past tense usage (38.9%) compared to theoretical papers (24.1%). The findings provide empirical evidence for prescriptive guidelines in academic writing instruction and demonstrate the effectiveness of computational approaches to linguistic analysis of scientific discourse.