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pro vyhledávání: '"Fujita, Sumio"'
Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations and synthetic data produced by consistency filtering to finetune a gener
Externí odkaz:
http://arxiv.org/abs/2308.02926
Big data mining is well known to be an important task for data science, because it can provide useful observations and new knowledge hidden in given large datasets. Proximity-based data analysis is particularly utilized in many real-life applications
Externí odkaz:
http://arxiv.org/abs/2208.14210
Influenza Surveillance using Search Engine, SNS, On-line Shopping, Q&A Service and Past Flu Patients
Influenza, an infectious disease, causes many deaths worldwide. Predicting influenza victims during epidemics is an important task for clinical, hospital, and community outbreak preparation. On-line user-generated contents (UGC), primarily in the for
Externí odkaz:
http://arxiv.org/abs/2104.06646
Autor:
Hisada, Shohei, Murayama, Taichi, Tsubouchi, Kota, Fujita, Sumio, Yada, Shuntaro, Wakamiya, Shoko, Aramaki, Eiji
[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 logs and user location information from smartphones. [Material and Metho
Externí odkaz:
http://arxiv.org/abs/2004.10100
Autor:
Gampa, Phanideep, Fujita, Sumio
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 BanditRank as it treats ranking as a contextual bandit problem. In the domai
Externí odkaz:
http://arxiv.org/abs/1910.10410
Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the i
Externí odkaz:
http://arxiv.org/abs/1808.09648
Autor:
Manabe, Tomohiro, Fujita, Sumio
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 adaptations designed for modeling documents with multiple fields. The Expanded
Externí odkaz:
http://arxiv.org/abs/1709.03260
Akademický článek
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Logs of the interactions with a search engine show that users often reformulate their queries. Examining these reformulations shows that recommendations that precise the focus of a query are helpful, like those based on expansions of the original que
Externí odkaz:
http://arxiv.org/abs/1204.2712
Autor:
Uehara, Makoto1 (AUTHOR), Fujita, Sumio2 (AUTHOR), Shimizu, Nobuyuki2 (AUTHOR), Liew, Kongmeng1 (AUTHOR), Wakamiya, Shoko1 (AUTHOR), Aramaki, Eiji1 (AUTHOR) aramaki@is.naist.jp
Publikováno v:
Scientific Reports. 9/3/2022, Vol. 12 Issue 1, p1-7. 7p.
