Jun Araki

812 citations
25 papers · 463 · h-index 10

Impact in

Papers in

    • Topic Modeling 18
    • Natural Language Processing Techniques 16
    • Semantic Web and Ontologies 3
    • Text Readability and Simplification 3
    • Text and Document Classification Technologies 2
    • Advanced Graph Neural Networks 2
    • Biomedical Text Mining and Ontologies 3

Jun Araki

22 papers receiving 435 citations

Peers

Jun Araki
Comparison fields: 5 of 69
  • Fuel Technology 18
  • Artificial Intelligence 342
  • Health Informatics 7
  • Geochemistry and Petrology 25
  • Management Science and Operations Research 41
Replace Peilin Zhou with:
Peilin Zhou China
Yao‐Tsung Chen Taiwan
S. Sivakumari India
Xueying Tang United States
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Qian Wan China
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Citations per year

Countries citing papers authored by Jun Araki

Since Specialization
Citations

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

Fields of papers citing papers by Jun Araki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Araki, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Araki Line = papers co-authored together Jun Araki links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202193
2 199771
3 202051
4
Supervised Within-Document Event Coreference using Information Propagation
201442
5
Events are Not Simple: Identity, Non-Identity, and Quasi-Identity
201341
6
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts.
201632
7 201531
8
Detecting Subevent Structure for Event Coreference Resolution
201429
9 199519
10
Open-Domain Event Detection using Distant Supervision
201818
11 20217
12
Interoperable Annotation of Events and Event Relations across Domains
20186
13 20145
14
CMU-LTI at KBP 2016 Event Nugget Track.
20163
15 20233
16 20183
17
CMU Multiple-choice Question Answering System at NTCIR-11 QA-Lab
20142
18 20142
19 19952
20 20181

About Jun Araki

Jun Araki is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Computer Vision and Pattern Recognition and Materials Chemistry, having authored 25 papers that have together received 463 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (16 papers), Semantic Web and Ontologies (3 papers), Biomedical Text Mining and Ontologies (3 papers), Text Readability and Simplification (3 papers), Multimodal Machine Learning Applications (2 papers), Text and Document Classification Technologies (2 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Fuel Technology (18 citations), Artificial Intelligence (342 citations), Health Informatics (7 citations), Geochemistry and Petrology (25 citations) and Management Science and Operations Research (41 citations). Jun Araki has collaborated with scholars based in United States, Japan and Qatar. Frequent co-authors include Teruko Mitamura, Haibo Ding, Zhengbao Jiang, Graham Neubig, Eduard Hovy, Zhengzhong Liu, Taisuke Maki, Kouichi Miura, Kazuhiro Mae and Antonios Anastasopoulos. Their work appears in journals such as Theory and applications of categories, Language Resources and Evaluation, Chemistry Letters, Energy & Fuels and Transactions of the Association for Computational Linguistics.

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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