Fusing lexicon information into Chinese characters, which has normally a number of
meanings, has been proven to be effective for Chinese Named Entity Recognition (NER) …

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a
sequence of input characters as well as all potential words that match a lexicon. Compared …

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 …

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

Recently, many works have tried to augment the performance of Chinese named entity
recognition (NER) using word lexicons. As a representative, Lattice-LSTM (Zhang and Yang …

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that
sequentially track character and word information have achieved great success. However …

Character-level Chinese named entity recognition (NER) that applies long short-term
memory (LSTM) to incorporate lexicons has achieved great success. However, this method …

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 …

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 …

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 …