Location reference identification from tweets during emergencies: A deep learning approach
Table 6: Result of the different combinations of CNN layers with filter sizes of 2-grams, 3-grams, and 4-grams
['Approach', 'Precision', 'Recall', '[ITALIC] F1-score', 'Hamming Loss', 'Jaccard Similarity', 'Exact matching']
The number of CNN layers was the most important parameter to decide, as the addition of one more CNN layer in 1-CNN outperformed all previous 1-CNN models. So, we did further experimentation by adding more CNN layers in the model. In order to do that, we developed models starting from 1-CNN to 4-CNN with two dense layers and filter sizes of 2-grams, 3-grams, and 4-grams together. We found that the use of 3-CNN yielded the best result. Our final model can be summarized as: ( i) Number of CNN layers = 3; (ii) Filter size = 2, 3, 4; (iii) Number of dense layers = 2; and (iv) and Dropout = 0.2.