Countries citing papers authored by Yoshihiro Matsuo
Since
Specialization
Citations
This map shows the geographic impact of Yoshihiro Matsuo'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 Yoshihiro Matsuo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoshihiro Matsuo more than expected).
Fields of papers citing papers by Yoshihiro Matsuo
This network shows the impact of papers produced by Yoshihiro Matsuo. 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 Yoshihiro Matsuo. The network helps show where Yoshihiro Matsuo may publish in the future.
Co-authorship network of co-authors of Yoshihiro Matsuo
This figure shows the co-authorship network connecting the top 25 collaborators of Yoshihiro Matsuo.
A scholar is included among the top collaborators of Yoshihiro Matsuo 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 Yoshihiro Matsuo. Yoshihiro Matsuo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hirano, Tôru, et al.. (2015). Automatic conversion of sentence-end expressions for utterance characterization of dialogue systems. Pacific Asia Conference on Language, Information, and Computation. 307–314.13 indexed citations
Izumi, Tomoko, Tomohide Shibata, Hisako Asano, Yoshihiro Matsuo, & Sadao Kurohashi. (2014). Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations. Language Resources and Evaluation. 1394–1400.1 indexed citations
7.
Higashinaka, Ryuichiro, Kenji Imamura, Toyomi Meguro, et al.. (2014). Towards an open-domain conversational system fully based on natural language processing. International Conference on Computational Linguistics. 928–939.103 indexed citations
8.
Nishikawa, Hitoshi, et al.. (2014). Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model. International Conference on Computational Linguistics. 1648–1659.13 indexed citations
9.
Saito, Itsumi, et al.. (2014). Morphological Analysis for Japanese Noisy Text based on Character-level and Word-level Normalization. International Conference on Computational Linguistics. 1773–1782.14 indexed citations
10.
Nishikawa, Hitoshi, et al.. (2013). A Pilot Study of Readability Prediction with Reading Time. Meeting of the Association for Computational Linguistics. 78–84.
11.
Imamura, Kenji, et al.. (2012). Constructing a Class-Based Lexical Dictionary using Interactive Topic Models. Language Resources and Evaluation. 2590–2595.1 indexed citations
12.
Imamura, Kenji, et al.. (2012). Entity Set Expansion using Interactive Topic Information. Waseda University Repository (Waseda University). 108–116.
13.
Nishikawa, Hitoshi, et al.. (2012). Text Summarization Model based on Redundancy-Constrained Knapsack Problem. International Conference on Computational Linguistics. 893–902.2 indexed citations
14.
Higashinaka, Ryuichiro, et al.. (2012). Creating an Extended Named Entity Dictionary from Wikipedia. International Conference on Computational Linguistics. 1163–1178.11 indexed citations
15.
Nishikawa, Hitoshi, Takaaki Hasegawa, Yoshihiro Matsuo, & Genichiro Kikui. (2010). Optimizing Informativeness and Readability for Sentiment Summarization. Meeting of the Association for Computational Linguistics. 325–330.16 indexed citations
16.
Hirano, Tôru, Hisako Asano, Yoshihiro Matsuo, & Genichiro Kikui. (2010). Recognizing Relation Expression between Named Entities based on Inherent and Context-dependent Features of Relational words. 409–417.3 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.