@inproceedings{zhang-etal-2020-covidex,
title = "Covidex: Neural Ranking Models and Keyword Search Infrastructure for the {COVID}-19 Open Research Dataset",
author = "Zhang, Edwin and
Gupta, Nikhil and
Tang, Raphael and
Han, Xiao and
Pradeep, Ronak and
Lu, Kuang and
Zhang, Yue and
Nogueira, Rodrigo and
Cho, Kyunghyun and
Fang, Hui and
Lin, Jimmy",
editor = "Chandrasekaran, Muthu Kumar and
de Waard, Anita and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hovy, Eduard and
Knoth, Petr and
Konopnicki, David and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.5",
doi = "10.18653/v1/2020.sdp-1.5",
pages = "31--41",
abstract = "We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.",
}
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<abstract>We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.</abstract>
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%0 Conference Proceedings
%T Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
%A Zhang, Edwin
%A Gupta, Nikhil
%A Tang, Raphael
%A Han, Xiao
%A Pradeep, Ronak
%A Lu, Kuang
%A Zhang, Yue
%A Nogueira, Rodrigo
%A Cho, Kyunghyun
%A Fang, Hui
%A Lin, Jimmy
%Y Chandrasekaran, Muthu Kumar
%Y de Waard, Anita
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hovy, Eduard
%Y Knoth, Petr
%Y Konopnicki, David
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-covidex
%X We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.
%R 10.18653/v1/2020.sdp-1.5
%U https://aclanthology.org/2020.sdp-1.5
%U https://doi.org/10.18653/v1/2020.sdp-1.5
%P 31-41
Markdown (Informal)
[Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset](https://aclanthology.org/2020.sdp-1.5) (Zhang et al., sdp 2020)
ACL
- Edwin Zhang, Nikhil Gupta, Raphael Tang, Xiao Han, Ronak Pradeep, Kuang Lu, Yue Zhang, Rodrigo Nogueira, Kyunghyun Cho, Hui Fang, and Jimmy Lin. 2020. Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset. In Proceedings of the First Workshop on Scholarly Document Processing, pages 31–41, Online. Association for Computational Linguistics.