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}
}
Visit hical.github.io for usage and installation instruction. For component specific details, check the README in their respective directory.
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
