TY - GEN
T1 - Domain-Oriented Chinese Named Entity Recognition Based on Enhanced Word-Word Relation Classification
AU - Chen, Yunru
AU - Huang, Yongyi
AU - Zhang, Huaping
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Traditional researches usually define named entity recognition (NER) as a sequence labeling task. But when it comes to specific domain, the entities are usually longer and contain prefixes representing domain knowledge, making their boundaries often more ambiguous compared to general entities. We draw inspiration from the word-word relation classification approach in the W2NER model and combined it with Conditional Layer Normalization (CLN), multigranularity dilated convolution, and a joint predictor that integrates Biaffine and MLP. On this basis, we enhance the model by adding Rotary Position Embedding (RoPE) to both the start and end sequences of the Biaffine classifier. Furthermore, we build a typical domain specific entity dataset in the defense domain. And we compare our model with the advanced baseline on our dataset and CLUENER2020 dataset, the experimental results verify our model outperforming the current baseline model.
AB - Traditional researches usually define named entity recognition (NER) as a sequence labeling task. But when it comes to specific domain, the entities are usually longer and contain prefixes representing domain knowledge, making their boundaries often more ambiguous compared to general entities. We draw inspiration from the word-word relation classification approach in the W2NER model and combined it with Conditional Layer Normalization (CLN), multigranularity dilated convolution, and a joint predictor that integrates Biaffine and MLP. On this basis, we enhance the model by adding Rotary Position Embedding (RoPE) to both the start and end sequences of the Biaffine classifier. Furthermore, we build a typical domain specific entity dataset in the defense domain. And we compare our model with the advanced baseline on our dataset and CLUENER2020 dataset, the experimental results verify our model outperforming the current baseline model.
KW - Conditional Layer Normalization
KW - Named Entity Recognition
KW - Rotary Position Embedding
KW - Word-Word Relation Classification
UR - https://www.scopus.com/pages/publications/105008368344
U2 - 10.1007/978-981-96-5123-8_12
DO - 10.1007/978-981-96-5123-8_12
M3 - Conference contribution
AN - SCOPUS:105008368344
SN - 9789819651221
T3 - Communications in Computer and Information Science
SP - 174
EP - 188
BT - Intelligent Multilingual Information Processing - 1st International Conference, IMLIP 2024, Proceedings
A2 - Zhang, Huaping
A2 - Shang, Jianyun
A2 - Su, Jinsong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Intelligent Multilingual Information Processing, IMLIP 2024
Y2 - 16 November 2024 through 17 November 2024
ER -