This map shows the geographic impact of Youngjoong Ko'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 Youngjoong Ko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youngjoong Ko more than expected).
This network shows the impact of papers produced by Youngjoong Ko. 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 Youngjoong Ko. The network helps show where Youngjoong Ko may publish in the future.
Co-authorship network of co-authors of Youngjoong Ko
This figure shows the co-authorship network connecting the top 25 collaborators of Youngjoong Ko.
A scholar is included among the top collaborators of Youngjoong Ko 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 Youngjoong Ko. Youngjoong Ko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ko, Youngjoong, et al.. (2018). Hierarchical attention based CNN-RNN networks for The Korean Speech-Act Analysis. 243–246.
9.
Ko, Youngjoong, et al.. (2018). Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph. International Conference on Computational Linguistics. 2704–2714.16 indexed citations
10.
Ko, Youngjoong, et al.. (2018). Expanding Korean/English Parallel Corpora using Back-translation for Neural Machine Translation. 470–473.1 indexed citations
11.
Ko, Youngjoong, et al.. (2016). Named Entity Recognition Using Bidirectional LSTM CRFs Based on the POS Tag Embedding and the Named Entity Distribution of Syllables. 105–110.2 indexed citations
12.
Ko, Youngjoong, et al.. (2016). Efficient Keyword Extraction and Text Summarization for Reading Articles on Smart Phone. Computing and Informatics / Computers and Artificial Intelligence. 34(4). 779–794.6 indexed citations
13.
Ko, Youngjoong, et al.. (2012). A Topic Classification Method Based on a Language Model Using the Structural Features of the Question-Answer Pair on cQA. Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong. 39(8). 664–671.1 indexed citations
14.
Ko, Youngjoong, et al.. (2011). Extracting Comparative Entities and Predicates from Texts Using Comparative Type Classification. Meeting of the Association for Computational Linguistics. 1636–1644.21 indexed citations
15.
Ko, Youngjoong, et al.. (2010). Automatic Construction of Class Hierarchies and Named Entity Dictionaries using Korean Wikipedia. Jeongbo gwahaghoe nonmunji. keompyuting ui silje. 16(4). 492–496.1 indexed citations
16.
Ko, Youngjoong, et al.. (2010). A Word Spacing System based on Syllable Patterns for Memory-constrained Devices. Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong. 37(8). 653–658.1 indexed citations
17.
Kim, Won‐Il, Youngjoong Ko, & Jungyun Seo. (2008). An Effective Adaptive Dialogue Strategy Using Reinforcement Loaming. Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong. 35(1). 33–40.
18.
Ko, Youngjoong & Jungyun Seo. (2008). Automatic Text Categorization based on Semi-Supervised Learning. Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong. 35(5). 325–334.
19.
Ko, Youngjoong, et al.. (2008). A Korean Sentence and Document Sentiment Classification System Using Sentiment Features. Jeongbo gwahaghoe nonmunji. keompyuting ui silje. 14(3). 336–340.6 indexed citations
20.
Ko, Youngjoong, et al.. (2001). Named Entity Recognition using Machine Learning Methods and Pattern-Selection Rules.. 229–236.13 indexed citations
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.