Entity Recognition Model Based on Multi-level Contextual Information Enhanced
WANG Tan
CHEN Jinguang
MA Lili
Abstract：Contextual word embeddings can bring more semantic and grammatical information for named entity recognition(NER),but the level of information only stays between words in one sentence,lacking the usage of morpheme level information of words and the overall theme information of the article.Addressing this issue,one named entity recognition model is proposed with contextual embeddings,in which multi-level context information is carried as a part of an input.In the MPNet training process,doc-ument context word embeddings is obtained via passing a sentence with its surrounding context.The contextual word embeddings and string embeddings are combined as the input of sequence labeling module.Experimental results show that the final model achieves the state-of-the-art results on the datasets,such as CoNLL-2003,CoNLL++and OntoNotes 5.0.
Keywords：natural language processingnamed entity recognitionpre-trained modelsbi-directional long short-term memoryconditional random field
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3162-3167 )