Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

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 …

Research on overlapped and discontinuous named entity recognition (NER) has received
increasing attention. The majority of previous work focuses on either overlapped or …

In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition
(nested NER). In our approach, token or text region embeddings are recursively inputted into …

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 …

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 …

Named entity recognition (NER) is a well-studied task in natural language processing.
However, the widely-used sequence labeling framework is usually difficult to detect entities …

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 …