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 …

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

Abstract Named Entity Recognition (NER), one of the most fundamental problems in natural
language processing, seeks to identify the boundaries and types of entities with specific …

Lexicon information and pre-trained models, such as BERT, have been combined to explore
Chinese sequence labelling tasks due to their respective strengths. However, existing …

Multi-modal named entity recognition (MNER) aims to discover named entities in free text
and classify them into pre-defined types with images. However, dominant MNER models do …

Recently, word enhancement has become very popular for Chinese Named Entity
Recognition (NER), reducing segmentation errors and increasing the semantic and …

The lack of word boundaries information has been seen as one of the main obstacles to
develop a high performance Chinese named entity recognition (NER) system. Fortunately …

A recently proposed lattice model has demonstrated that words in character sequence can
provide rich word boundary information for character-based Chinese NER model. In this …

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of
natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as …

Named entity recognition (NER) is a fundamental task in Chinese natural language
processing (NLP) tasks. Recently, Chinese clinical NER has also attracted continuous …