Boosting Named Entity Recognition with Neural Character Embeddings
Table 2: Neural Network Hyperparameters.
['[BOLD] Parameter', '[BOLD] Parameter Name', '[BOLD] CharWNN', '[BOLD] WNN', '[BOLD] CharNN']
In most of our experiments, we use the same hyperparameters used by dos Santos and Zadrozny (2014) for part-of-speech tagging. The only exception is the learning rate for SPA CoNLL-2002, which we set to 0.005 in order to avoid divergence. We use the development sets to determine the number of training epochs, which is six for HAREM and sixteen for SPA CoNLL-2002. We compare CharWNN with two similar neural network architectures: CharNN and WNN. CharNN is equivalent to CharWNN without word embeddings, i.e., it uses character-level embeddings only. WNN is equivalent to CharWNN without character-level embeddings, i.e., it uses word embeddings only. The capitalization feature has five possible values: all lowercased, first uppercased, all uppercased, contains an uppercased letter, and all other cases. We use suffix of size three. In our experiments, both capitalization and suffix embeddings have dimension five.