Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

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 …

Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems
from sequence labeling to span prediction. Despite its preliminary effectiveness, the span …

Multi-choice reading comprehension is a challenging task to select an answer from a set of
candidate options when given passage and question. Previous approaches usually only …

In many scenarios, named entity recognition (NER) models severely suffer from unlabeled
entity problem, where the entities of a sentence may not be fully annotated. Through …

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic
properties where entities may be extracted according to how they are used and placed in the …

Despite the success of ChatGPT, its performances on most NLP tasks are still well below the
supervised baselines. In this work, we looked into the causes, and discovered that its subpar …

In the era of information explosion, named entity recognition (NER) has attracted
widespread attention in the field of natural language processing, as it is fundamental to …

Named entity recognition (NER) is frequently addressed as a sequence classification task
where each input consists of one sentence of text. It is nevertheless clear that useful …