Deep Neural Networks for Czech Multi-label Document Classification
Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]
['[BOLD] Word number', '100', '200', '300', '400', '500']
Finally, we tested our network with different numbers of input words and with varying size of the embedding vectors. We can conclude that the 400 words that we chose at the beginning was a reasonable choice. However, it is beneficial to use longer embedding vectors. It must be noted that the further increasing of the embedding size has a strong impact on the computation time and might be not practical for real-world applications.