Data overlap is a significant challenge in the task of entity–relation triple extraction. This task
includes two research lines, line one first identifies entities and then predicts relations while …

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 …

Recent advances in natural language representation have enabled the internal state of an
upstream trained model to migrate to downstream tasks such as named entity recognition …

One of the applications of Natural Language Processing (NLP) is to process free text data for
extracting information. Information extraction has various forms like Named Entity …

We explore multiple important choices that have not been analyzed in conjunction regarding
active learning for token classification using transformer networks. These choices are:(i) how …

Variational recurrent autoencoder model (VRAE) is an appealing technique for capturing the
variabilities underlying complex sequential data, which is realized by introducing high-level …

Biomedical Named Entity Recognition (BioNER) is a crucial task in extracting entities from
biomedical literature. It plays a key role as the initial step in various biomedical information …

Few-shot NER aims to identify entities of target types with only limited number of illustrative
instances. Unfortunately, few-shot NER is severely challenged by the intrinsic precise …

Named entity recognition (NER) is a fundamental task for natural language processing,
which aims to detect mentions of real-world entities from text and classifying them into …

For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms
are quite different. Previous research has demonstrated that the two paradigms have clear …