Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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 …

So far, named entity recognition (NER) has been involved with three major types, including
flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied …

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) task aims at identifying entities from a piece of text that
belong to predefined semantic types such as person, location, organization, etc. The state-of …

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing,
concerned with identifying spans of text expressing references to entities. NER research is …

The task of named entity recognition (NER) is normally divided into nested NER and flat
NER depending on whether named entities are nested or not. Models are usually separately …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an
encoder in models solving the named entity recognition (NER) task. Recently, the …

With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …