31 citations · 36 across the 9 of their papers we have counts for
5 papers · 1 filter
Effect of Model Merging in Domain-Specific Ad-hoc Retrieval
Taiga Sasaki, Takehiro Yamamoto, Hiroaki Ohshima +1
In this study, we evaluate the effect of model merging in ad-hoc retrieval tasks. Model merging is a technique that combines the diverse characteristics of multiple models. We hypo…
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval
Haoxiang Shi, Sumio Fujita, Tetsuya Sakai
Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations an…
Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan
Shohei Hisada, Taichi Murayama, Kota Tsubouchi +4
[Background] Two clusters of coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan in February 2020. To capture the clusters, this study employs Web search query lo…
BanditRank: Learning to Rank Using Contextual Bandits
Phanideep Gampa, Sumio Fujita
We propose an extensible deep learning method that uses reinforcement learning to train neural networks for offline ranking in information retrieval (IR). We call our method Bandit…
A Short Note on Proximity-based Scoring of Documents with Multiple Fields
Tomohiro Manabe, Sumio Fujita
The BM25 ranking function is one of the most well known query relevance document scoring functions and many variations of it are proposed. The BM25F function is one of its adaptati…