Attention-based multi-level feature fusion for named entity recognition

Zhiwei Yang, Hechang Chen*, Jiawei Zhang, Jing Ma, Yi Chang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceeding

15 Citations (Scopus)


Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.

Original languageEnglish
Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9780999241165
Publication statusPublished - Jan 2021
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: 7 Jan 202115 Jan 2021

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Abbreviated titleIJCAI-PRICAI 2020
Internet address

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Information Extraction
  • Tagging, chunking, and parsing
  • Named Entities
  • Natural Language Processing


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