Neural Named Entity Recognition from Subword Units
Table 4: F1 scores of the combined models with different units being used.
['[EMPTY]', '[BOLD] Char', '[BOLD] Phoneme', '[BOLD] Byte', '[BOLD] Char + Phoneme', '[BOLD] Char + Byte', '[BOLD] Phoneme + Byte', '[BOLD] All']
Combined models. As can be seen, in this setting there are again additive gains by using several subword units compared to using only one. Depending on the language, phonemes yield the best results in combination with either characters or bytes, indicating that phonemes are useful for named entity recognition, which, to the best of out knowledge, is first explored in this work. The reason might be that they contribute to modelling phonology and/or morphology, thus improving performance in particular for out-of-vocabulary words. For three out of the four languages, the combination of characters, phonemes and word-level embeddings achieved the best results.