Abstract: A named entity recognition algorithm BAM-TDNN based on bidirectional two-dimensional convolution and pre-training model is proposed to address the problem of semantic information weakening layer by layer when processing nested structures in named entity recognition.This algorithm first uses four word embedding strategies,namely BERT,distance,locality,and attention embedding,to extract semantic features at different levels within a sentence,and converts semantic features at multiple levels into two-dimensional semantic representations,better capturing semantic information between nested structures.Secondly,the Bi TDNN model is used to learn the long-range semantic dependencies of entities in sentences,expand the receptive field of span representation,provide more accurate semantic information between nested entities,and better understand the semantic associations between nested entities.Through evaluation on four public datasets,experimental results show that the proposed named entity recognition algorithm has achieved good performance on multiple entity recognition datasets.The accuracy,recall,and F1 value of BAM-TDNN on the ACE2005 dataset is 86.83%,87.93%,and 86.83%,respectively.The accuracy,recall,and F1 value on the GENIA dataset is 86.52%,82.37%,and 84.36%,respectively.The accuracy,recall,and F1 value on the CoNLL2003 dataset is 92.24%,93.72%,and 91.97%,respectively.

Key words: Named entity recognition, Four-word embedding strategy, BERT, Bi-TDNN