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Found 5 papers, 0 papers with code
Continuous Active Learning Using Pretrained Transformers
Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall information retrieval, where the objective is to retrieve substantially all relevant documents.
The eDiscovery Medicine Show
The practice of bloodletting gradually fell into disfavor as a growing body of scientific evidence showed its ineffectiveness and demonstrated the effectiveness of various pharmaceuticals for the prevention and treatment of certain diseases.
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback.
Impact of Feature Selection on Micro-Text Classification
Social media datasets, especially Twitter tweets, are popular in the field of text classification.
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review
We enhance the autonomy of the continuous active learning method shown by Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted review, in which documents from a collection are retrieved and reviewed, using relevance feedback, until substantially all of the relevant documents have been reviewed.
