Young-In Song

1.1k total citations
41 papers, 745 citations indexed

About

Young-In Song is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Young-In Song has authored 41 papers receiving a total of 745 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 20 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Young-In Song's work include Topic Modeling (31 papers), Natural Language Processing Techniques (18 papers) and Advanced Text Analysis Techniques (12 papers). Young-In Song is often cited by papers focused on Topic Modeling (31 papers), Natural Language Processing Techniques (18 papers) and Advanced Text Analysis Techniques (12 papers). Young-In Song collaborates with scholars based in South Korea, China and United States. Young-In Song's co-authors include Chin-Yew Lin, Jing Liu, Hae‐Chang Rim, Long Wang, Yueheng Sun, Gao Cong, Xinying Song, Fan Zhang, Hsiao-Wuen Hon and Jung‐Tae Lee and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Information Processing & Management and ACM Transactions on Information Systems.

In The Last Decade

Young-In Song

38 papers receiving 690 citations

Peers

Young-In Song
Comparison fields: 5 of 45
  • Artificial Intelligence 609
  • Information Systems 442
  • Computer Science Applications 123
  • Statistical and Nonlinear Physics 110
  • Management Science and Operations Research 49
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Citations per field, relative to Young-In Song
Young-In Song · 1×
Citations per year, relative to Young-In Song
Young-In Song · 1×

Countries citing papers authored by Young-In Song

Since Specialization
Citations

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

Fields of papers citing papers by Young-In Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young-In Song. 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 Young-In Song. The network helps show where Young-In Song may publish in the future.

Co-authorship network of co-authors of Young-In Song

This figure shows the co-authorship network connecting the top 25 collaborators of Young-In Song. A scholar is included among the top collaborators of Young-In Song 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 Young-In Song. Young-In Song 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 4
2 11
3 1
4 2
5
MSRA at NTCIR-10 1CLICK-2.
1
6 162
7 7
8
Microsoft Research Asia at the NTCIR-9 1CLICK Task
5
9
Overview of NTCIR-9 1CLICK.
13
10 19
11 58
12
Mining Name Translations from Entity Graph Mapping
13
13
Microsoft Research Asia with Redmond at the NTCIR-8 Community QA Pilot Task
2
14 2
15
Text-Confidence Feature Based Quality Evaluation Model for Knowledge Q&A Documents
0
16 38
17 6
18 15
19
Title Named Entity Recognition based on Automatically Constructed Context Patterns and Entity Dictionary
2
20
Korea University Question Answering System at TREC 2004.
6

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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