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https://github.com/hical/HiCAL
HiCAL is a system for efficient high-recall retrieval with an adaptable assessing interface.
https://github.com/hical/HiCAL
active-learning cal document-assessment high-recall information-retrieval machine-learning search-engine test-collection
Last synced: about 7 hours ago
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HiCAL is a system for efficient high-recall retrieval with an adaptable assessing interface.
- Host: GitHub
- URL: https://github.com/hical/HiCAL
- Owner: hical
- License: gpl-3.0
- Created: 2018-07-07T00:28:23.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-26T20:29:10.000Z (almost 2 years ago)
- Last Synced: 2024-08-02T13:22:19.893Z (3 months ago)
- Topics: active-learning, cal, document-assessment, high-recall, information-retrieval, machine-learning, search-engine, test-collection
- Language: Python
- Homepage: https://hical.github.io/
- Size: 5.81 MB
- Stars: 35
- Watchers: 5
- Forks: 11
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Want to find all relevant documents?
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](https://dl.acm.org/doi/10.1145/3209978.3210176) or visit the project's [website](https://hical.github.io/).
```
@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](https://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:
+ [UWaterlooMDS at the TREC 2018 Common Core Track](https://cs.uwaterloo.ca/~h435zhan/TREC2018) *TREC 2018*
+ Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark Smucker, Gordon Cormack and Maura Grossman. [Effective User Interaction for High-Recall Retrieval: Less is More](https://cs.uwaterloo.ca/~h435zhan/CIKM2018) *CIKM 2018*
+ Nimesh Ghelani, Gordon Cormack, and Mark Smucker. [Refresh Strategies in Continuous Active Learning](http://ceur-ws.org/Vol-2127/paper6-profs.pdf) *SIGIR 2018 workshop on Professional Search*
+ Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark Smucker, Gordon Cormack and Maura Grossman. [A System for Efficient High-Recall Retrieval](https://dl.acm.org/citation.cfm?id=3209978.3210176) *Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018)*
+ Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Angshuman Ghosh, Mark Smucker, Gordon Cormack and Maura Grossman. [UWaterlooMDS at the TREC 2017 Common Core Track](https://trec.nist.gov/pubs/trec26/papers/UWaterlooMDS-CC.pdf) *(TREC 2017)*
+ Haotian Zhang, Gordon Cormack, Maura Grossman and Mark Smucker. [Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval](https://arxiv.org/abs/1803.08988)
License
--------
[](http://www.gnu.org/licenses/gpl.html)