Su Nam Kim

44 papers receiving 1.1k citations

Peers

Su Nam Kim
Comparison fields: 5 of 67
  • Artificial Intelligence 1.1k
  • Information Systems 200
  • Molecular Biology 163
  • Computer Vision and Pattern Recognition 36
  • Statistics, Probability and Uncertainty 30
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Emily Pitler United States
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Citations per field
00.5×2.8×
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Citations per year

Countries citing papers authored by Su Nam Kim

Since Specialization
Citations

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

Fields of papers citing papers by Su Nam Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su Nam Kim. 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 Su Nam Kim. The network helps show where Su Nam Kim may publish in the future.

Co-authorship network of co-authors of Su Nam Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Su Nam Kim. A scholar is included among the top collaborators of Su Nam Kim 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 Su Nam Kim. Su Nam Kim 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
#WorkIndexed citations
1
A Noisy Channel Approach to Error Correction in Spoken Referring Expressions
4
2
Evaluation of the Scusi? Spoken Language Interpretation System -- A Case Study
3
3
The Utility of Discourse Structure in Identifying Resolved Threads in Technical User Forums
4
4
Classifying Dialogue Acts in Multi-party Live Chats
13
5
Extracting Keywords from Multi-party Live Chats
9
6
Predicting Thread Discourse Structure over Technical Web Forums
36
7 136
8
Tagging and Linking Web Forum Posts
51
9
SemEval-2 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions
23
10
Intelligent Linux Information Access by Data Mining: the ILIAD Project
8
11
Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction
23
12
Classifying Dialogue Acts in One-on-One Live Chats
53
13
SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles
205
14 239
15
The Use of Topic Representative Words in Text Categorization
2
16
Multi-way classification of semantic relations between pairs of nominals
10
17
Extracting Domain-Specific Words - A Statistical Approach
6
18
MRD-based Word Sense Disambiguation: Further Extending Lesk
11
19
Benchmarking Noun Compound Interpretation
3
20
Disambiguating noun compounds
8

About Su Nam Kim

Su Nam Kim is a scholar working on Artificial Intelligence, Information Systems and Developmental and Educational Psychology, having authored 44 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (34 papers), Natural Language Processing Techniques (32 papers) and Speech and dialogue systems (19 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Information Systems (200 citations) and Health Informatics (6 citations). Su Nam Kim has collaborated with scholars based in Australia, Singapore and Canada. Frequent co-authors include Timothy Baldwin, Min‐Yen Kan, Olena Medelyan, Lawrence Cavedon, David Martínez, Preslav Nakov, Stan Śzpakowicz, Diarmuid Ó Séaghdha, Zornitsa Kozareva and Marco Pennacchiotti. Their work appears in journals such as BMC Bioinformatics, Language Resources and Evaluation and Computer Speech & Language.

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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