Wei Lu

1.8k citations
98 papers · 1.0k · 1 hit paper · h-index 16

Impact in

Papers in

Wei Lu

92 papers receiving 997 citations

Wei Lu's Hit Papers

A comparative study of automated legal text classification using random forests and deep learning 2021 · 143 citations
1430+1+3Years since publication4080120

Peers

Wei Lu
Comparison fields: 5 of 141
  • Statistics, Probability and Uncertainty 206
  • Artificial Intelligence 420
  • Information Systems and Management 73
  • Health Informatics 13
  • General Social Sciences 33
Replace Ying Ding with:
Ying Ding China
Giovanni Colavizza Netherlands
Andreas Strotmann Canada
Jin Mao China
Qing Ke China
Massimo Franceschet Italy
Yongjun Zhu South Korea
Martin Szomszor United Kingdom
Nils C. Newman United States
Dangzhi Zhao Canada
Wei Lu relative to Ying Ding China Ying Ding's profile →
Citations per field
00.5×3.6×
Ying Ding · 1×
Citations per year

Countries citing papers authored by Wei Lu

Since Specialization
Citations

This map shows the geographic impact of Wei Lu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wei Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Lu more than expected).

Fields of papers citing papers by Wei Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei Lu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wei Lu. The network helps show where Wei Lu may publish in the future.

Co-authors

The 25 scholars most cited alongside Wei Lu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Wei Lu Line = papers co-authored together Wei Lu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 98 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A comparative study of automated legal text classification using random forests and deep learning
Hit paper breakdown →
2021143
2 202088
3 202157
4 202050
5 201447
6 202031
7 202228
8 200226
9 202124
10 202221
11 201821
12 202020
13 202119
14 202118
15 202018
16 202217
17 202314
18 202114
19 202114
20 202313

About Wei Lu

Wei Lu is a scholar working on Artificial Intelligence, Information Systems, Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 98 papers that have together received 1.0k indexed citations. Recurring topics across this work include Topic Modeling (24 papers), scientometrics and bibliometrics research (24 papers), Advanced Text Analysis Techniques (19 papers), Complex Network Analysis Techniques (13 papers), Data Quality and Management (11 papers), Expert finding and Q&A systems (9 papers), Natural Language Processing Techniques (9 papers) and Semantic Web and Ontologies (9 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (206 citations), Artificial Intelligence (420 citations), Information Systems and Management (73 citations), Health Informatics (13 citations) and General Social Sciences (33 citations). Wei Lu has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Yong Huang, Qikai Cheng, Yi Bu, Haihua Chen, Jiangping Chen, Junhua Ding, Lei Wu, Xin Li, Zhifeng Liu and Xiaoguang Wang. Their work appears in journals such as Information Processing & Management, Scientometrics, Journal of Information Science, Journal of Informetrics and The Electronic Library.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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