TY - GEN
T1 - A Mixed Semantic Features Model for Chinese NER with Characters and Words
AU - Chang, Ning
AU - Zhong, Jiang
AU - Li, Qing
AU - Zhu, Jiang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Named Entity Recognition (NER) is an essential part of many natural language processing (NLP) tasks. The existing Chinese NER methods are mostly based on word segmentation, or use the character sequences as input. However, using a single granularity representation would suffer from the problems of out-of-vocabulary and word segmentation errors, and the semantic content is relatively simple. In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding. Different from other models, our method combines character and word features at the sequence level, and the attention mechanism computes similarity on the total sequence consisted of characters and words. The character semantic information and the structure of words work together to improve the accuracy of word boundary segmentation and solve the problem of long-phrase combination. We validate our model on MSRA and Weibo corpora, and experiments demonstrate that our model can significantly improve the performance of the Chinese NER task.
AB - Named Entity Recognition (NER) is an essential part of many natural language processing (NLP) tasks. The existing Chinese NER methods are mostly based on word segmentation, or use the character sequences as input. However, using a single granularity representation would suffer from the problems of out-of-vocabulary and word segmentation errors, and the semantic content is relatively simple. In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding. Different from other models, our method combines character and word features at the sequence level, and the attention mechanism computes similarity on the total sequence consisted of characters and words. The character semantic information and the structure of words work together to improve the accuracy of word boundary segmentation and solve the problem of long-phrase combination. We validate our model on MSRA and Weibo corpora, and experiments demonstrate that our model can significantly improve the performance of the Chinese NER task.
KW - Chinese named entity recognition
KW - Entity boundary segmentation
KW - Mixed semantic feature
KW - Self-attention
UR - http://www.scopus.com/inward/record.url?scp=85084003089&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45439-5_24
DO - 10.1007/978-3-030-45439-5_24
M3 - 会议稿件
AN - SCOPUS:85084003089
SN - 9783030454388
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 356
EP - 368
BT - Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Proceedings
A2 - Jose, Joemon M.
A2 - Yilmaz, Emine
A2 - Magalhães, João
A2 - Martins, Flávio
A2 - Castells, Pablo
A2 - Ferro, Nicola
A2 - Silva, Mário J.
PB - Springer
T2 - 42nd European Conference on IR Research, ECIR 2020
Y2 - 14 April 2020 through 17 April 2020
ER -