Boosting Named Entity Recognition with Neural Character Embeddings
Table 5: Comparison of different NNs for the HAREM I corpus.
['[BOLD] NN', '[BOLD] Features', '[BOLD] Total Scenario  [BOLD] Prec.', '[BOLD] Total Scenario  [BOLD] Rec.', '[BOLD] Total Scenario  [BOLD] F1', '[BOLD] Selective Scenario  [BOLD] Prec.', '[BOLD] Selective Scenario  [BOLD] Rec.', '[BOLD] Selective Scenario  [BOLD] F1']
The results in this table were computed using the CoNLL-2002 evaluation script. We report results in two scenarios: total and selective. In the total scenario, all ten categories are taken into account when scoring the systems. In the selective scenario, only five chosen categories (Person, Organization, Location, Date and Value) are taken into account. that CharWNN and WNN that uses two additional handcrafted features have similar results. We think that by increasing the training data, CharWNN has the potential to learn better character embeddings and outperform WNN, like happens in the SPA CoNLL-2002 corpus, which is larger than the HAREM I corpus. Again, CharNN and WNN that uses word embeddings only, do not achieve results competitive with the results of the networks that jointly use word-level and character-level information.