Q
Qian Li
Researcher at Beihang University
Publications - 17
Citations - 271
Qian Li is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Event (computing). The author has an hindex of 2, co-authored 9 publications receiving 35 citations.
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A Survey on Text Classification: From Shallow to Deep Learning
TL;DR: A taxonomy for text classification according to the text involved and the models used for feature extraction and classification is created, dealing with both the technical developments and benchmark datasets that support tests of predictions.
Journal ArticleDOI
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou,Qian Li,Chen Li,Guan Wang,Kaichao Zhang,Cheng Ji,Qi Yan,Lifang 何麗芳 He,H. Peng,Jianxin Li,Jia Wu,Ziwei Liu,Pengtao Xie,Caiming Xiong,Jian Pei,Philip S. Yu,Lichao Sun Michigan State University,B. University,Lehigh University,M. University,Nanyang Technological University,University of California at San Diego,D. University,Universityof Chicago,Salesforce AI Research +24 more
TL;DR: Pretrained Foundation Models (PFMs) as mentioned in this paper are regarded as the foundation for various downstream tasks with different data modalities, such as text, image, graph, as well as other data modality.
Journal ArticleDOI
A Survey on Text Classification: From Traditional to Deep Learning
TL;DR: A taxonomy for text classification according to the text involved and the models used for feature extraction and classification is created, dealing with both the technical developments and benchmark datasets that support tests of predictions.
Proceedings ArticleDOI
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
TL;DR: This paper proposed a novel joint learning framework with cascade decoding for overlapping event extraction, termed as CasEE, which sequentially performs type detection, trigger extraction and argument extraction, where the overlapped targets are extracted separately conditioned on the specific former prediction.
Posted Content
A Text Classification Survey: From Shallow to Deep Learning
TL;DR: A taxonomy for text classification according to the text involved and the models used for feature extraction and classification is created, dealing with both the technical developments and benchmark datasets that support tests of predictions.