Improving Relation Extraction with Knowledge-attention
Table 2: Results of adding NER embeddings and entity categorical embeddings to the multi-channel attention (MCA) integrated model.
['Model', 'P', 'R', 'F1']
Performance gain is the lowest for multi-channel attention (MCA). However, the model is more flexible in the way that features from other information sources can be easily added to the model to further improve its performance. We use dimensionality of 30 and 60 for NER and entity categorical embeddings respectively, and the two embedding matrixes are learned by the neural network. Results show that adding NER and entity categorical information to MCA integrated model improves F1 score by 0.2 and 0.5 respectively, and adding both improves precision significantly, resulting a new best F1 score.