2 research outputs found
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
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 the first step for Chinese NER. However, models based
on word-level embeddings and lexicon features often suffer from segmentation
errors and out-of-vocabulary (OOV) words. In this paper, we investigate a
Convolutional Attention Network called CAN for Chinese NER, which consists of a
character-based convolutional neural network (CNN) with local-attention layer
and a gated recurrent unit (GRU) with global self-attention layer to capture
the information from adjacent characters and sentence contexts. Also, compared
to other models, not depending on any external resources like lexicons and
employing small size of char embeddings make our model more practical.
Extensive experimental results show that our approach outperforms
state-of-the-art methods without word embedding and external lexicon resources
on different domain datasets including Weibo, MSRA and Chinese Resume NER
dataset.Comment: This paper is accepted by NAACL-HLT 2019. The code is available at
https://github.com/microsoft/vert-papers/tree/master/papers/CAN-NE
Self-attention-based BiGRU and capsule network for named entity recognition
Named entity recognition(NER) is one of the tasks of natural language
processing(NLP). In view of the problem that the traditional character
representation ability is weak and the neural network method is unable to
capture the important sequence information. An self-attention-based
bidirectional gated recurrent unit(BiGRU) and capsule network(CapsNet) for NER
is proposed. This model generates character vectors through bidirectional
encoder representation of transformers(BERT) pre-trained model. BiGRU is used
to capture sequence context features, and self-attention mechanism is proposed
to give different focus on the information captured by hidden layer of BiGRU.
Finally, we propose to use CapsNet for entity recognition. We evaluated the
recognition performance of the model on two datasets. Experimental results show
that the model has better performance without relying on external dictionary
information