🧑‍🔬 Yoshiyasu Takefuji

🏛️ Affiliations

🎓Musashino UniversityTokyo · Japan2021–2026 · 269 papersEmployment 2021–2026 · Professor, Data Sciencecurrent🎓Keio UniversityTokyo · Japan1974–2026 · 202 papersEducation 1974–1978 · BS, Electrical EngineeringEducation 1978–1980 · Master Science, Electrical EngineeringEducation 1980–1983 · Ph.D., Electrical EngineeringEmployment 1992–2021 · Professor, Environment and Information Studiescurrent🏢SciencePark CorporationZama Shi · Japan2026 · 18 paperscurrent🎓Case Western Reserve UniversityCleveland · United States1988–2005 · 93 papersEmployment 1988–1996 · tenured Associate Professor in 1992, Electrical Engineering and Applied Physics🎓Keio University Shonan FujisawaFujisawa · Japan1992–2021 · 48 papers🎓University of South CarolinaColumbia · United States1985–2003 · 13 papersEmployment 1985–1988 · Associate Professor, Computer Engineering🔬National Institute of InformaticsTokyo · Japan2021–2023 · 2 papers🔬National Institute of Information and Communications TechnologyTokyo · Japan2007 · 2 papers🎓University of South FloridaTampa · United States1983–1985 · 1 paperEmployment 1983–1985 · Assistant Professor, Computer Science

🏛️Q11652759Nagasaki · JapanEducation

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation (2016 · 428 citations)
   Named Entity Disambiguation (NED) links ambiguous mentions in text, such as "Washington", to the correct entry in a knowledge base, here Wikipedia.
2. Learning Distributed Representations of Texts and Entities from Knowledge Base (2017 · 125 citations)
   The paper presents Neural Text-Entity Encoder (NTEE), a model that places sentences or paragraphs and knowledge base entities in one shared vector space.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Joint Learning of the Embedding of Words and Entities for Named Entity DisambiguationConference on Computational Natural Language Learning (CoNLL)Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al.2016 · 428 citations
2. Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yetIkuya Yamada et al.2020 · 210 citations
3. Learning Distributed Representations of Texts and Entities from Knowledge BaseTransactions of the Association for Computational Linguistics (TACL)Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al.2017 · 125 citations

🤝 Co-authors here

3 co-authors with a page, the most papers together first.

• Hideaki Takeda ×2
• Hiroyuki Shindo ×2
• Ikuya Yamada ×2

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Computational Natural Language Learning (CoNLL): CoNLL 2016
• Transactions of the Association for Computational Linguistics (TACL): Transactions of the Association for Computational Linguistics (TACL) 2017
• Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

🔗 Cited by

Papers of our library cite their works 170 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated 30 October 2026

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