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Canasai Kruengkrai

Canasai Kruengkrai has 2 papers in our library, in 2016–2020, most of them on Coreference resolution, Low-resource languages; often with Bosheng Ding, Chunyan Miao, Jong-Hoon Oh.

2 papers in our library · 2016–2020

OpenAlex · h-index 12 · 71 works · 710 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 89 times in our library · ⭐ Paper Stars ranking: #4402

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🔬RIKENWako · Japan2022–2026 · 6 paperscurrent🔬National Institute of Information and Communications TechnologyTokyo · Japan2005–2019 · 15 papers🔬National Institute of InformaticsTokyo · Japan2020–2024 · 12 papers🎓Kasetsart UniversityBangkok · Thailand2002–2004 · 4 papers🎓Tohoku UniversitySendai · Japan2012–2016 · 3 papers🎓Kobe UniversityKobe · Japan2009 · 2 papers

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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Low-resource languages · 1 workCoreference resolution · 1 work

 Works by year

2020s

20201 paperDAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks

2010s

20161 paperIntra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network

 Papers

The most cited first (OpenAlex and our library).

  1. DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020 · 203 citations)
    Sequence tagging tasks label every token, so they are easily hurt by noisy augmented data. Common augmentation strategies also depend on resources that low-resource languages often lack, such as WordNet, bilingual corpora or in-domain weak taggers. DAGA addresses this by generating new labeled sentences from scratch.
  2. Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network (2016 · 51 citations)
    The paper addresses intra-sentential subject zero anaphora resolution in Japanese. This is the task of linking an omitted subject of a predicate to an antecedent in the same sentence, a frequent phenomenon in pro-drop languages. The authors use a Multi-column Convolutional Neural Network (MCNN). It scores every…

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 89 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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