HiCAL is a system for efficient high-recall retrieval. The system allows retrieving and assessing relevant documents and provides high data processing performance and a user-friendly document assessment interface.
Our model was evaluated on the standard TREC dataset: TREC Core 2017 Track.
Visit hical.github.io for usage and installation instruction. For component specific details, check the README in their respective directory.
This repo contains the implementation of High-Recall Information Retrieval system, described in the following papers:
-
Nimesh Ghelani, Gordon Cormack, and Mark Smucker. Refresh Strategies in Continuous Active Learning SIGIR 2018 workshop on Professional Search
-
Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark Smucker, Gordon Cormack and Maura Grossman. [Effective User Interaction for High-Recall Retrieval: Less is More] Submitting
-
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)
-
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)
-
Haotian Zhang, Gordon Cormack, Maura Grossman and Mark Smucker. Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
