Ai Ti Aw

436 total citations
20 papers, 217 citations indexed

About

Ai Ti Aw is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ai Ti Aw has authored 20 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in Ai Ti Aw's work include Topic Modeling (18 papers), Natural Language Processing Techniques (16 papers) and Multimodal Machine Learning Applications (5 papers). Ai Ti Aw is often cited by papers focused on Topic Modeling (18 papers), Natural Language Processing Techniques (16 papers) and Multimodal Machine Learning Applications (5 papers). Ai Ti Aw collaborates with scholars based in Singapore, United States and China. Ai Ti Aw's co-authors include Thuy Vu, Min Zhang, Nancy F. Chen, Lee Sheldon, Zhengyuan Liu, Min Zhang, Hongfei Jiang, Jun Sun, Kui Wu and Chew Lim Tan and has published in prestigious journals such as ACM Transactions on Asian Language Information Processing, National University of Singapore and arXiv (Cornell University).

In The Last Decade

Ai Ti Aw

16 papers receiving 195 citations

Peers

Ai Ti Aw
Comparison fields: 5 of 31
  • Artificial Intelligence 206
  • Computer Vision and Pattern Recognition 27
  • Information Systems 19
  • Molecular Biology 15
  • Signal Processing 5
Replace Hiroshi Noji with:
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Shamil Chollampatt Singapore
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Tagyoung Chung United States
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Hiroshi Noji Japan View profile →
Citations per field, relative to Ai Ti Aw
Ai Ti Aw · 1×
Citations per year, relative to Ai Ti Aw
Ai Ti Aw · 1×

Countries citing papers authored by Ai Ti Aw

Since Specialization
Citations

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

Fields of papers citing papers by Ai Ti Aw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ai Ti Aw. 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 Ai Ti Aw. The network helps show where Ai Ti Aw may publish in the future.

Co-authorship network of co-authors of Ai Ti Aw

This figure shows the co-authorship network connecting the top 25 collaborators of Ai Ti Aw. A scholar is included among the top collaborators of Ai Ti Aw 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 Ai Ti Aw. Ai Ti Aw 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 2
3 0
4 0
5 17
6 3
7 2
8
Data Diversification: A Simple Strategy For Neural Machine Translation
8
9
Data Diversification: An Elegant Strategy For Neural Machine Translation.
6
10 12
11 5
12 55
13 16
14
Personalized Normalization for a Multilingual Chat System
4
15
An Unsupervised and Data-Driven Approach for Spell Checking in Vietnamese OCR-scanned Texts
3
16 0
17 25
18
Term Extraction Through Unithood and Termhood Unification
26
19
I 2 R Chinese-English Translation System for IWSLT 2007
6
20
A tree-to-tree alignment-based model for statistical machine translation
27

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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