Results 41 to 50 of about 250,954 (220)

Dealing with Typos for BERT-based Passage Retrieval and Ranking [PDF]

Conference on Empirical Methods in Natural Language Processing, 2021
Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers.
Shengyao Zhuang, G. Zuccon
semanticscholar   +1 more source

FAIR: Fairness‐aware information retrieval evaluation [PDF]

J. Assoc. Inf. Sci. Technol., 2021
With the emerging needs of creating fairness‐aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions.
Ruoyuan Gao, Yingqiang Ge, C. Shah
semanticscholar   +1 more source

Temporal ranking for fresh information retrieval [PDF]

Proceedings of the sixth international workshop on Information retrieval with Asian languages -, 2003
In business, the retrieval of up-to-date, or fresh, information is very important. It is difficult for conventional search engines based on a centralized architecture to retrieve fresh information, because they take a long time to collect documents via Web robots.
Nobuyoshi Sato, Minoru Uehara, Yoshifumi Sakai   +2 more
openaire   +1 more source

Explainable Information Retrieval

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval.
Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra   +4 more
semanticscholar   +1 more source

Neural ranking models for document retrieval [PDF]

Information Retrieval Journal, 2021
Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in
M. Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin   +3 more
semanticscholar   +1 more source

Research on Hybrid Retrieval-augmented Dual-tower Model [PDF]

Jisuanji kexue
In the vanguard of knowledge retrieval,particularly in scenarios involving large language models(LLMs),research emphasis has shifted toward employing pure vector retrieval techniques for efficient capture of pertinent information.This information is ...
GAO Hongkui, MA Ruixiang, BAO Qihao, XIA Shaojie, QU Chongxiao
doaj   +1 more source

Re-ranking vehicle re-identification with orientation-guide query expansion

International Journal of Distributed Sensor Networks, 2022
Vehicle re-identification, which aims to retrieve information regarding a vehicle from different cameras with non-overlapping views, has recently attracted extensive attention in the field of computer vision owing to the development of smart cities. This
Xue Zhang, Xiushan Nie, Ziruo Sun, Xiaofeng Li, Chuntao Wang, Peng Tao, Sumaira Hussain   +6 more
doaj   +1 more source

Information Retrieval in an Infodemic: The Case of COVID-19 Publications

Journal of Medical Internet Research, 2021
BackgroundThe COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19–related corpora are being created, sometimes with ...
Douglas Teodoro, Sohrab Ferdowsi, Nikolay Borissov, Elham Kashani, David Vicente Alvarez, Jenny Copara, Racha Gouareb, Nona Naderi, Poorya Amini   +8 more
doaj   +1 more source

Text-to-Image GAN-Based Scene Retrieval and Re-Ranking Considering Word Importance

IEEE Access, 2019
In this paper, we propose a novel scene retrieval and re-ranking method based on a text-to-image Generative Adversarial Network (GAN). The proposed method generates an image from an input query sentence based on the text-to-image GAN and then retrieves a
Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama   +3 more
doaj   +1 more source

Online Learning to Rank for Information Retrieval [PDF]

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
During the past 10--15 years offline learning to rank has had a tremendous influence on information retrieval, both scientifically and in practice. Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the ...
Grotov, A., de Rijke, M.
openaire   +2 more sources