Kentaro Inui

5.5k total citations
235 papers, 2.7k citations indexed

About

Kentaro Inui is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kentaro Inui has authored 235 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 216 papers in Artificial Intelligence, 38 papers in Information Systems and 19 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kentaro Inui's work include Topic Modeling (178 papers), Natural Language Processing Techniques (145 papers) and Advanced Text Analysis Techniques (42 papers). Kentaro Inui is often cited by papers focused on Topic Modeling (178 papers), Natural Language Processing Techniques (145 papers) and Advanced Text Analysis Techniques (42 papers). Kentaro Inui collaborates with scholars based in Japan, United States and United Kingdom. Kentaro Inui's co-authors include Yūji Matsumoto, Sadao Kurohashi, Ryu Iida, Tetsuji Nakagawa, Nozomi Kobayashi, Jun Suzuki, Naoaki Okazaki, Tatsuki Kuribayashi, Shun Kiyono and Naoya Inoue and has published in prestigious journals such as PLoS ONE, Information Processing & Management and Engineering Applications of Artificial Intelligence.

In The Last Decade

Kentaro Inui

215 papers receiving 2.4k citations

Peers

Kentaro Inui
Noah Constant United States
Aliaksei Severyn Italy
Kevin Gimpel United States
Veselin Stoyanov United States
Jean Y. Wu United States
Donghong Ji China
Sadao Kurohashi Japan
Horacio Saggion Spain
Anders Søgaard Denmark
Daniel Cer United States
Noah Constant United States
Kentaro Inui
Citations per year, relative to Kentaro Inui Kentaro Inui (= 1×) peers Noah Constant

Countries citing papers authored by Kentaro Inui

Since Specialization
Citations

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

Fields of papers citing papers by Kentaro Inui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kentaro Inui

This figure shows the co-authorship network connecting the top 25 collaborators of Kentaro Inui. A scholar is included among the top collaborators of Kentaro Inui 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 Kentaro Inui. Kentaro Inui 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
1.
Inui, Kentaro, et al.. (2025). SPIRIT: Patching Speech Language Models against Jailbreak Attacks. 14514–14531.
2.
Inui, Kentaro, et al.. (2025). How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders. 13458–13470. 1 indexed citations
3.
Kuribayashi, Tatsuki, et al.. (2023). Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?. 1351–1362. 3 indexed citations
4.
Kobayashi, Goro, et al.. (2023). Transformer Language Models Handle Word Frequency in Prediction Head. 4523–4535. 1 indexed citations
5.
6.
Tanaka, Yuko, et al.. (2023). Who Does Not Benefit from Fact-checking Websites?. 1–17. 1 indexed citations
7.
Ouchi, Hiroki, et al.. (2022). N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models. 637–644. 1 indexed citations
8.
9.
Kaneko, Masahiro, Masato Mita, Shun Kiyono, Jun Suzuki, & Kentaro Inui. (2020). Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction. 4248–4254. 83 indexed citations
10.
Kobayashi, Goro, et al.. (2020). Attention is Not Only a Weight: Analyzing Transformers with Vector Norms. 7057–7075. 79 indexed citations
11.
Inui, Kentaro, et al.. (2018). Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis. arXiv (Cornell University). 94–106. 6 indexed citations
12.
Kobayashi, Sosuke, et al.. (2018). Unsupervised Learning of Style-sensitive Word Vectors. 572–578. 6 indexed citations
13.
Kiyono, Shun, Sho Takase, Jun Suzuki, et al.. (2018). Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models. 74–81. 4 indexed citations
14.
Sasaki, Akira, et al.. (2015). Annotating Geographical Entities on Microblog Text. 85–94. 6 indexed citations
15.
Ohtake, Kiyonori, et al.. (2013). NICT Disaster Information Analysis System. International Joint Conference on Natural Language Processing. 29–32. 6 indexed citations
16.
Inoue, Naoya, et al.. (2012). Online Large-margin Weight Learning for First-order Logic-based Abduction. IEICE Technical Report; IEICE Tech. Rep.. 112(279). 143–150. 2 indexed citations
17.
Nichols, Eric, et al.. (2011). TU Group at NTCIR9-RITE: Leveraging Diverse Lexical Resources for Recognizing Textual Entailment. NTCIR. 1 indexed citations
18.
Iida, Ryu, et al.. (2001). Kura: A Lexico-Structural Paraphrasing Engine.. 763–764. 2 indexed citations
19.
Inui, Takashi & Kentaro Inui. (2001). Committee-based Decision Making in Probabilistic Partial Parsing. 42(12). 3160–3172. 1 indexed citations
20.
Shirai, Kiyoaki, Kentaro Inui, Takenobu Tokunaga, & Hozumi Tanaka. (1998). An Empirical Evaluation on Statistical Parsing of Japanese Sentences Using Lexical Association Statistics. Tokyo Tech Research Repository (Tokyo Institute of Technology). 80–86. 6 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.

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