English Literature
English literature studies are increasingly leveraging computational methods to analyze vast textual corpora, moving beyond traditional qualitative approaches. Current research focuses on applying natural language processing (NLP) techniques, including transformer-based models like BERT and GPT, to tasks such as sentiment analysis, topic modeling, named entity recognition, and even literary translation and generation. This interdisciplinary approach allows for large-scale analyses of literary works, revealing patterns and insights previously inaccessible, ultimately enriching both literary scholarship and our understanding of language and human expression.
Papers
Literature Meets Data: A Synergistic Approach to Hypothesis Generation
Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, Chenhao Tan
Analyzing Nobel Prize Literature with Large Language Models
Yang Zhenyuan, Liu Zhengliang, Zhang Jing, Lu Cen, Tai Jiaxin, Zhong Tianyang, Li Yiwei, Zhao Siyan, Yao Teng, Liu Qing, Yang Jinlin, Liu Qixin, Li Zhaowei, Wang Kexin, Ma Longjun, Zhu Dajiang, Ren Yudan, Ge Bao, Zhang Wei, Qiang Ning, Zhang Tuo, Liu Tianming
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Zhengliang Liu, Yiwei Li, Qian Cao, Junwen Chen, Tianze Yang, Zihao Wu, John Hale, John Gibbs, Khaled Rasheed, Ninghao Liu, Gengchen Mai, Tianming Liu
Artificial intelligence and the limits of the humanities
Włodzisław Duch