Convolutional Network Entity Missing Detection Method Combined with Gated Mechanism
Abstrak
The adequacy of the entity information directly affects the applications that depend on textual entity information,while conventional entity recognition models can only identify the existing entities.The task of the entity missing detection,defined as a sequence labeling task,aims at finding the location where the entity is missing.In order to construct training dataset,three corres-ponding methods are proposed.We introduce an entity missing detection method combining the convolutional neural network with the gated mechanism and the pre-trained language model.Experiments show that the F1 scores of this model are 80.45% for the PER entity,83.02% for the ORG entity,and 86.75% for the LOC entity.The model performance exceeds the other LSTM-based named entity recognition model.It is found that there is a correlation between the accuracy of the model and the word frequency of the annotated characters.
Topik & Kata Kunci
Penulis (1)
YE Han, LI Xin, SUN Haichun
Akses Cepat
- Tahun Terbit
- 2023
- Sumber Database
- DOAJ
- DOI
- 10.11896/jsjkx.220400126
- Akses
- Open Access ✓