Dynamic Graph Transformer for Implicit Tag Recognition
Textual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous work. However, few works pay their attention to the implicit information. For example, a financial news article mentioned {``}Apple Inc.{''} may be also related to Samsung, even though Samsung is not explicitly mentioned in this article. This work presents a novel dynamic graph transformer that distills the textual information and the entity relations on the fly. Experimental results confirm the effectiveness of our approach to implicit tag recognition.
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Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Graph Transformer •
Label Smoothing •
LapEigen •
Laplacian PE •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer
