In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

When asked, large language models~(LLMs) like ChatGPT claim that they can assist with
relevance judgments but it is not clear whether automated judgments can reliably be used in …

ACM: Digital Library: Communications of the ACM ACM Digital Library Communications of the
ACM Volume 67, Number 4 (2024), Pages 31-34 Opinion: Who Determines What Is Relevant …

Active learning seeks to achieve strong performance with fewer training samples. It does this
by iteratively asking an oracle to label newly selected samples in a human-in-the-loop …

Named entity recognition (NER) is a fundamental task for several important applications
such as knowledge base construction and semantic search. So far, the focus has been on …

In the machine learning field, the challenge of effectively learning with limited data has
grown increasingly crucial. Active Learning (AL) algorithms play a significant role in this by …

Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to
actively query instance annotations from which it expects to learn the most. Despite …

Learning semantic-rich representations from unlabeled time series data with intricate
dynamics is a notable challenge. Traditional contrastive learning techniques predominantly …

Active learning is a commonly used approach that reduces the labeling effort required to
train deep neural networks. However, the effectiveness of current active learning methods is …