Neural Named Entity Recognition from Subword Units
Table 3: F1 scores of the subword-only models with different units being used. The model with the three subword units combined achieved best performance across languages, except for FR.
['[EMPTY]', '[BOLD] Char', '[BOLD] Phoneme', '[BOLD] Byte', '[BOLD] Char + Phoneme', '[BOLD] Char + Byte', '[BOLD] Phoneme + Byte', '[BOLD] All']
Subwords only models. When used individually, different subword units yield the best results for different languages. For example, for English, the best individual subword unit is phoneme (with 0.67 points in F1 more than character), while character-level unit achieved best results for French. Here it must be noted that the phoneme lexicon for French had much lower coverage than the one for English, which explains the low F1 score. When several subword units are combined, results improve for all languages, and except for French, the best results are achieved when using all of the subword units combined. For French, the best combination is characters and bytes, i.e. without using phonemes, which we again attribute to the low coverage of the French phoneme lexicon. To explore further whether these improvements are indeed due to using several subword units rather than the increased dimensionality of the hidden embedding representation, we trained models for the different languages using a single subword unit, however, with higher embedding and LSTM hidden dimensions.