As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

We present a bi-encoder framework for named entity recognition (NER), which applies
contrastive learning to map candidate text spans and entity types into the same vector …

We study the problem of training named entity recognition (NER) models using only distantly-
labeled data, which can be automatically obtained by matching entity mentions in the raw …

Few-shot NER needs to effectively capture information from limited instances and transfer
useful knowledge from external resources. In this paper, we propose a self-describing …

Recent studies in deep learning have shown significant progress in named entity
recognition (NER). Most existing works assume clean data annotation, yet a fundamental …

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 …

More recently, Named Entity Recognition hasachieved great advances aided by pre-
trainingapproaches such as BERT. However, currentpre-training techniques focus on …

Distant supervision tackles the data bottleneck in NER by automatically generating training
instances via dictionary matching. Unfortunately, the learning of DS-NER is severely …

Text semantic matching is a fundamental task that has been widely used in various
scenarios, such as community question answering, information retrieval, and …