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HiCAL is a system for efficient high-recall retrieval. The system allows retrieving and assessing relevant documents with high data processing performance and a user-friendly document assessment interface.
To learn more about HiCAL, read our paper or visit the project's website.
@inproceedings{10.1145/3209978.3210176,
author = {Abualsaud, Mustafa and Ghelani, Nimesh and Zhang, Haotian and Smucker, Mark D. and Cormack, Gordon V. and Grossman, Maura R.},
title = {A System for Efficient High-Recall Retrieval},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210176},
doi = {10.1145/3209978.3210176},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {1317–1320},
numpages = {4},
keywords = {high-recall, systematic review, electronic discovery},
location = {Ann Arbor, MI, USA},
series = {SIGIR ’18}
}
Install
Visit hical.github.io for usage and installation instruction. For component specific details, check the README in their respective directory.
Relevant Papers
Learn more about HiCAL and its performance in the following papers:
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Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark Smucker, Gordon Cormack and Maura Grossman. Effective User Interaction for High-Recall Retrieval: Less is More CIKM 2018
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Nimesh Ghelani, Gordon Cormack, and Mark Smucker. Refresh Strategies in Continuous Active Learning SIGIR 2018 workshop on Professional Search
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Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark Smucker, Gordon Cormack and Maura Grossman. A System for Efficient High-Recall Retrieval Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018)
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Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Angshuman Ghosh, Mark Smucker, Gordon Cormack and Maura Grossman. UWaterlooMDS at the TREC 2017 Common Core Track (TREC 2017)
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Haotian Zhang, Gordon Cormack, Maura Grossman and Mark Smucker. Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval