Lawrence Shih
Impact in
- Artificial Intelligence top 2%
- Text and Document Classification Technologies
- Advanced Text Analysis Techniques
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Imbalanced Data Classification Techniques
- Natural Language Processing Techniques
- Information Systems top 2%
- Spam and Phishing Detection
- Web Data Mining and Analysis
Papers in
-
- Text and Document Classification Technologies 4
- Machine Learning and Algorithms 2
- Natural Language Processing Techniques 1
- Bayesian Modeling and Causal Inference 1
- Advanced Text Analysis Techniques 1
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- Spam and Phishing Detection 2
- Web Data Mining and Analysis 1
- Co-authors
- David R. Karger (4 shared papers)Jason D. M. Rennie (3 shared papers)Jaime Teevan (1 shared paper)Yu-Han Chang (2 shared papers)David L. Greene (1 shared paper)
- Journals
- Complexity (2 papers)International Conference on Machine Learning (2 papers)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United States
In The Last Decade
Lawrence Shih
7 papers receiving 655 citations
Lawrence Shih's Hit Papers
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 482
- Information Systems 269
- Signal Processing 55
- Software 16
- Health Information Management 17
Countries citing papers authored by Lawrence Shih
This map shows the geographic impact of Lawrence Shih'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 Lawrence Shih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lawrence Shih more than expected).
Fields of papers citing papers by Lawrence Shih
This network shows the impact of papers produced by Lawrence Shih. 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 Lawrence Shih. The network helps show where Lawrence Shih may publish in the future.
Co-authors
The 5 scholars most cited alongside Lawrence Shih, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Tackling the poor assumptions of naive bayes text classifiers Hit paper breakdown → | 2003 | 631 |
| 2 | 2004 | 53 | |
| 3 | Text bundling: statistics-based data reduction | 2003 | 23 |
| 4 | Not Too Hot, Not Too Cold: The Bundled-SVM is Just Right! | 2002 | 8 |
| 5 | 1998 | 4 | |
| 6 | 1998 | 3 | |
| 7 | Learning Classes Correlated to a Hierarchy | 2003 | 2 |
About Lawrence Shih
Lawrence Shih is a scholar working on Artificial Intelligence, Information Systems, Economics and Econometrics, Infectious Diseases and Organic Chemistry, having authored 7 papers that have together received 724 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (4 papers), Spam and Phishing Detection (2 papers), Machine Learning and Algorithms (2 papers), Complex Systems and Time Series Analysis (2 papers), Web Data Mining and Analysis (1 paper), Natural Language Processing Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (482 citations), Information Systems (269 citations), Signal Processing (55 citations), Software (16 citations) and Health Information Management (17 citations). Lawrence Shih has collaborated with scholars based in United States. Frequent co-authors include David R. Karger, Jason D. M. Rennie, Jaime Teevan, Yu-Han Chang and David L. Greene. Their work appears in journals such as Complexity, International Conference on Machine Learning and DSpace@MIT (Massachusetts Institute of Technology).
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.