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HiCAL is a system for efficient high-recall retrieval that allows retrieving and assessing relevant documents and provides high data processing performance with a user-friendly document assessment interface.
There are two retrieval components in HiCAL.
Search: the search component allows you to search for documents using a search engine. Judgments made in the search component are used to train the CAL model.
Continuous Active Learning (CAL): CAL is an iterative feedback process that uses a machine learning classifier to train on relevance judgments made by the user.
The CAL interface presents the user with top scoring unjudged documents in the collection. After each iteration of judging and re-training, the learning model improves and returns the next most likely relevant documents to judge.
To learn more about HiCAL, visit the project's website.
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