Junji Tomita

23 papers receiving 149 citations

Peers

Junji Tomita
Comparison fields: 5 of 39
  • Artificial Intelligence 137
  • Computer Vision and Pattern Recognition 25
  • Social Psychology 22
  • Information Systems 22
  • Experimental and Cognitive Psychology 14
Replace Rolandos Alexandros Potamias with:
Rolandos Alexandros Potamias United Kingdom
Masahiro Araki Japan
Varada Kolhatkar Canada
Diptesh Kanojia India
Cheongjae Lee South Korea
John Niekrasz United States
Jamin Shin Hong Kong
Ashwin Kalyan United States
Ruvan Weerasinghe Sri Lanka
Iñigo Casanueva United Kingdom
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Citations per field
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Rolandos Alexandros Potamias · 1×
Citations per year

Countries citing papers authored by Junji Tomita

Since Specialization
Citations

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

Fields of papers citing papers by Junji Tomita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junji Tomita

This figure shows the co-authorship network connecting the top 25 collaborators of Junji Tomita. A scholar is included among the top collaborators of Junji Tomita 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 Junji Tomita. Junji Tomita 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
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Unsupervised Domain Adaptation of Language Models for Reading Comprehension
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6
Sparse Contextual Sentence Representation for Fast Information Retrieval
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7 8
8 24
9
Creating Large-Scale Argumentation Structures for Dialogue Systems
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10
Predicting Nods by using Dialogue Acts in Dialogue
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11 1
12 4
13 19
14 14
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Quick-adapting and Flexible Autonomous Robot System
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16
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
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17
Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
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18
Expanding the Recall of Relation Extraction by Bootstrapping
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19 4
20
Interactive Web Search by Graphical Query Refinement.
8

About Junji Tomita

Junji Tomita is a scholar working on Artificial Intelligence, Human-Computer Interaction and Social Psychology, having authored 29 papers that have together received 172 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Speech and dialogue systems (11 papers) and Natural Language Processing Techniques (9 papers). The work is most often cited by research in Artificial Intelligence (137 citations), Human-Computer Interaction (14 citations) and Computer Vision and Pattern Recognition (25 citations). Junji Tomita has collaborated with scholars based in Japan. Frequent co-authors include Kyosuke Nishida, Ryuichiro Higashinaka, Itsumi Saito, Hisako Asano, Shiro Kumano, Kazuhiro Otsuka, Ryo Ishii, Megumi Ishii, Genichiro Kikui and Atsushi Otsuka. Their work appears in journals such as Language Resources and Evaluation, IEICE Transactions on Information and Systems and Multimodal Technologies and Interaction.

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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