Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction
Table 1: Performance with different vector sizes of the GloVe Word Embeddings: 50, 100, 200 and 300 (we called them GloVe-(SIZE), respectively).
['[BOLD] Embedding', '[BOLD] Size', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score', '[BOLD] Epochs']
To test the impact of word embeddings, we perform experiments with the pre-trained Stanford’s GloVe Embeddings using all the word embedding sizes available, i.e., 50, 100, 200 and 300. The training of the network takes about 30 seconds to perform a full epoch with all the GloVe Embeddings. To stop the training, we used Keras’ own embedded early stopping rule, which halts training when the training loss does not decrease for two consecutive epochs. obtained by our system when trained using different sizes of the word embeddings. We can note that the best results are obtained with embedding size of 100; however, the embedding sizes of 200 and 300 obtain a very close result in term of F1-Score. The scores seem to show an interesting pattern: in fact, looking at the results, we see that the precision increases with embedding size, while recall decreases from size 100 onwards.