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Soichiro Fujita

dblp:245/8770 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 67% Data mining · 33%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › video captioning
dense video captioning
0.412020
SODA: Story Oriented Dense Video Captioning Evaluation Framework · ECCV (6) 2020
Multimedia analysis and retrieval
video captioning
0.412020
SODA: Story Oriented Dense Video Captioning Evaluation Framework · ECCV (6) 2020
Information retrieval › ranking › text ranking
comment ranking
0.412019
Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019
Data mining
dataset construction
0.412019
Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019
Information retrieval
ranking
0.412019
Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019

Methods — techniques the papers use, named apart from their topics

pairwise ranking model · 0.4
YearPublicationVenuePosition
2020 SODA: Story Oriented Dense Video Captioning Evaluation Framework
Soichiro Fujita, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
ECCV (6)1
2020 Unsupervised Ensemble of Ranking Models for News Comments Using Pseudo Answers
Soichiro Fujita, Hayato Kobayashi, Manabu Okumura
ECIR (2)1
2019 Dataset Creation for Ranking Constructive News Comments
abstract
Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task.However, most of them considered users' positive feedback, such as "Like"-button clicks, as a quality measure.In this paper, we address directly evaluating the quality of comments on the basis of "constructiveness," separately from user feedback.To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores).Our experiments clarify (a) C-scores are not always related to users' positive feedback and (b) the performance of pairwise ranking models tends to be more enhanced by the variation in comments than that in articles.
Soichiro Fujita, Hayato Kobayashi, Manabu Okumura
ACL (1)1