neuralVietNLP: neural NLP Tools for Vietnamese Language Processing
Bang 3: Grid search for hyper-parameters.
['Hyper-parameter cemb dim (char embedding)', 'Search Space 50', 'Search Space 100', 'Search Space 500']
Model: We apply the current most well-known neural network architecture for NER task of \newciteMa:2016 with no modification in its architecture, namely, BiLSTM-CRF+CNN-char (BiLC3). Only in the embedding layer, a different set of word embeddings is used to evaluate their effectiveness. Regarding experiments, we perform a grid search for hyper-parameters and select the best parameters on the validation set to run on the test set. We also follow the same setting as in Vu et al. to use the last 2000 records in the training data as the validation set. Moreover, due to the availability of the VnCoreNLP code, we also retrain their model with our pre-trained embeddings (VnCoreNLP∗).