Boosting Named Entity Recognition with Neural Character Embeddings
Table 3: Comparison of different NNs for the SPA CoNLL-2002 corpus.
['[BOLD] NN', '[BOLD] Features', '[BOLD] Dev. Set  [BOLD] Prec.', '[BOLD] Dev. Set  [BOLD] Rec.', '[BOLD] Dev. Set  [BOLD] F1', '[BOLD] Test Set  [BOLD] Prec.', '[BOLD] Test Set  [BOLD] Rec.', '[BOLD] Test Set  [BOLD] F1']
CharWNN achieves the best precision, recall and F1 in both development and test sets. For the test set, the F1 of CharWNN is 3 points larger than the F1 of the WNN that uses two additional handcrafted features: suffixes and capitalization. This result suggests that, for the NER task, the character-level embeddings are as or more effective as the two character-level features used in WNN. Similar results were obtained by dos Santos and Zadrozny (2014) in the POS tagging task. Both, WNN that uses word embeddings only and CharNN, do not achieve results competitive with the results of the networks that jointly use word-level and character-level information. This is not surprising, since it is already known in the NLP community that jointly using word-level and character-level features is important to perform named entity recognition.