Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition


  • Chenxiao Dou Nanhu Academy of Electronics and Information Technology
  • Xianghui Sun BeiKe
  • Yaoshu Wang Shenzhen Institute of Computing Sciences, Shenzhen University
  • Yunjie Ji Beike
  • Baochang Ma Beike
  • Xiangang Li Beike



SNLP: Syntax -- Tagging, Chunking & Parsing, SNLP: Lexical & Frame Semantics, Semantic Parsing, SNLP: Other Foundations of Speech & Natural Language Processing, SNLP: Phonology, Morphology, Word Segmentation, SNLP: Text Mining


In recent years, many researchers have leveraged structural information from dependency trees to improve Named Entity Recognition (NER). Most of their methods take dependency-tree labels as input features for NER model training. However, such dependency information is not inherently provided in most NER corpora, making the methods with low usability in practice. To effectively exploit the potential of word-dependency knowledge, motivated by the success of Multi-Task Learning on cross-domain NER, we investigate a novel NER learning method incorporating cross-domain Dependency Parsing (DP) as its auxiliary learning task. Then, considering the high consistency of word-dependency relations across domains, we present an unsupervised domain-adapted method to transfer word-dependency knowledge from high-resource domains to low-resource ones. With the help of cross-domain DP to bridge different domains, both useful cross-domain and cross-task knowledge can be learned by our model to considerably benefit cross-domain NER. To make better use of the cross-task knowledge between NER and DP, we unify both tasks in a shared network architecture for joint learning, using Maximum Mean Discrepancy(MMD). Finally, through extensive experiments, we show our proposed method can not only effectively take advantage of word-dependency knowledge, but also significantly outperform other Multi-Task Learning methods on cross-domain NER. Our code is open-source and available at




How to Cite

Dou, C., Sun, X., Wang, Y., Ji, Y., Ma, B., & Li, X. (2023). Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 12737-12744.



AAAI Technical Track on Speech & Natural Language Processing