Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies
biomedical entities with special meanings, such as people, places, and organizations, as …

There is a recent interest in investigating few-shot NER, where the low-resource target
domain has different label sets compared with a resource-rich source domain. Existing …

Named Entity Recognition (NER) is the task of identifying spans that represent entities in
sentences. Whether the entity spans are nested or discontinuous, the NER task can be …

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks,
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …

User reviews of mobile apps provide a communication channel for developers to perceive
user satisfaction. Many app features that users have problems with are usually expressed by …

Information extraction (IE) systems aim to automatically extract structured information, such
as named entities, relations between entities, and events, from unstructured texts. While …

Conditional random fields (CRF) for label decoding has become ubiquitous in sequence
labeling tasks. However, the local label dependencies and inefficient Viterbi decoding have …

Document-level label consistency is an effective indicator that different occurrences of a
particular token sequence are very likely to have the same entity types. Previous work …

Sequence labeling is a fundamental task in natural language processing and has been
widely studied. Recently, RNN-based sequence labeling models have increasingly gained …

Multi-task learning (MTL) has been studied recently for sequence labeling. Typically,
auxiliary tasks are selected specifically in order to improve the performance of a target task …