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 …

State-of-the-art sequence labeling systems traditionally require large amounts of task-
specific knowledge in the form of hand-crafted features and data pre-processing. In this …

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 …

Past work in relation extraction has focused on binary relations in single sentences. Recent
NLP inroads in high-value domains have sparked interest in the more general setting of …

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

Linguistic sequence labeling is a general approach encompassing a variety of problems,
such as part-of-speech tagging and named entity recognition. Recent advances in neural …

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

Recent papers have shown that neural networks obtain state-of-the-art performance on
several different sequence tagging tasks. One appealing property of such systems is their …

Named entity recognition (NER) is an important task in natural language processing area,
which needs to determine entities boundaries and classify them into pre-defined categories …