Hiroya Takamura

117 papers receiving 1.1k citations

Peers

Hiroya Takamura
Comparison fields: 5 of 73
  • Artificial Intelligence 1.1k
  • Information Systems 175
  • Computer Vision and Pattern Recognition 135
  • Sociology and Political Science 58
  • Statistical and Nonlinear Physics 47
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Maíra Gatti de Bayser Brazil
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Citations per year

Countries citing papers authored by Hiroya Takamura

Since Specialization
Citations

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

Fields of papers citing papers by Hiroya Takamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hiroya Takamura

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroya Takamura. A scholar is included among the top collaborators of Hiroya Takamura 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 Hiroya Takamura. Hiroya Takamura 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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11
Exploring the Influence of Spelling Errors on Lexical Variation Measures.
2
12 8
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Discriminative Analysis of Linguistic Features for Typological Study
9
14
Subtree Extractive Summarization via Submodular Maximization
17
15
Construction of Emotional Lexicon Using Potts Model
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16
Topic Estimation for Microblogs Taking into Account the Relationships between Adjacent Tweets
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17 108
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Extracting Emotional Polarity of Words using Spin Model (Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining) -- (Session 12: Text Mining 2)
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19
Classification of responses to open-ended questions with machine learning and hand-grafted rules: Automatic occupation coding methods
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20
Modeling Category Structures with a Kernel Function
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About Hiroya Takamura

Hiroya Takamura is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 131 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (88 papers), Natural Language Processing Techniques (80 papers) and Advanced Text Analysis Techniques (36 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Information Systems (175 citations) and Computer Vision and Pattern Recognition (135 citations). Hiroya Takamura has collaborated with scholars based in Japan, United States and Taiwan. Frequent co-authors include Manabu Okumura, Takashi Inui, Hidetaka Kamigaito, Daisuke Ikeda, Yūji Matsumoto, Yusuke Miyao, Ryohei Sasano, Ryu Iida, Kentaro Inui and Masaaki Nagata. Their work appears in journals such as Scientific Reports, Language Resources and Evaluation and Knowledge and Information Systems.

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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