Wai Lam

4.1k total citations
72 papers, 2.0k citations indexed

About

Wai Lam is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Wai Lam has authored 72 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Artificial Intelligence, 22 papers in Information Systems and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Wai Lam's work include Topic Modeling (35 papers), Natural Language Processing Techniques (22 papers) and Advanced Text Analysis Techniques (13 papers). Wai Lam is often cited by papers focused on Topic Modeling (35 papers), Natural Language Processing Techniques (22 papers) and Advanced Text Analysis Techniques (13 papers). Wai Lam collaborates with scholars based in Hong Kong, United States and China. Wai Lam's co-authors include Fahiem Bacchus, Lidong Bing, Piji Li, Deng Cai, Xin Li, Simone Teufel, Dragomir Radev, Horacio Saggion, Ruizhang Huang and Elliott F. Drábek and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Knowledge-Based Systems and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).

In The Last Decade

Wai Lam

67 papers receiving 1.8k citations

Peers

Wai Lam
Comparison fields: 5 of 116
  • Artificial Intelligence 1.7k
  • Information Systems 448
  • Computer Vision and Pattern Recognition 182
  • Molecular Biology 149
  • Management Science and Operations Research 147
Replace Yun Xiong with:
Yun Xiong China
Klaus Brinker Germany
Ningyu Zhang China
Dejing Dou United States
Andy Chu Canada
Michael Cochez Germany
Jun Sakuma Japan
Wanli Zuo China
Adiwijaya Adiwijaya Indonesia
Shengli Wu United Kingdom
Yun Xiong China View profile →
Citations per field, relative to Wai Lam
Wai Lam · 1×
Citations per year, relative to Wai Lam
Wai Lam · 1×

Countries citing papers authored by Wai Lam

Since Specialization
Citations

This map shows the geographic impact of Wai Lam'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 Wai Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wai Lam more than expected).

Fields of papers citing papers by Wai Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wai Lam. 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 Wai Lam. The network helps show where Wai Lam may publish in the future.

Co-authorship network of co-authors of Wai Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Wai Lam. A scholar is included among the top collaborators of Wai Lam based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wai Lam. Wai Lam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 8
3 0
4 22
5 1
6 0
7 13
8 12
9
Incorporating Pseudo-Parallel Data for Quantifiable Sequence Editing.
1
10
CUIS Team at TREC 2018 CAR Track.
1
11 5
12
Abstractive Multi-Document Summarization via Phrase Selection and
2
13
Building Knowledge Base for Reading from Encyclopedia.
1
14
Using Semantic Relations with World Knowledge for Question Answering.
7
15 3
16
Context-based generic cross-lingual retrieval of documents and automated summaries: Research Articles
1
17
Pattern Based Customized Learning for TREC Genomics Track Categorization Task.
1
18
Developing Infrastructure for the Evaluation of Single and Multi-document Summarization Systems in a Cross-lingual Environment.
24
19
Constructing Text Filters Based on Bayesian Network Learning.
1
20 17

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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