Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

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 …

Neural sequence labeling is widely adopted for many Natural Language Processing (NLP)
tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and …

With the rapid growth of Internet penetration, more and more people choose the Internet to
express their views on topics of interest. In recent years, named entity recognition (NER) is …

Support vector regression (SVR) has been widely used for reliability modeling and
prediction in various engineering practices. In order to improve the accuracy and robustness …

With the continuous development of target recognition technology, people pay more and
more attention to the cost of sample generation, tag addition and network training. Active …

Recent alignment algorithms such as direct preference optimization (DPO) have been
developed to improve the safety of large language models (LLMs) by training these models …

We conducted a scoping review for active learning in the domain of natural language
processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as …

Industry 4.0 provides emergence to what is called the “Smart Factory.” Industry 4.0 (IR 4.0) is
a growing phenomenon for automation and information sharing in industrial technology …

Sequential labeling is a task predicting labels for each token in a sequence, such as Named
Entity Recognition (NER). NER tasks aim to extract entities and predict their labels given a …