A Byte-sized Approach to Named Entity Recognition
Table 2: F1 scores across Bio-ID categories. Best entity results, excluding last column, are bolded.
['[BOLD] Entity type', '[BOLD] Word CRF', '[BOLD] Word NN', '[BOLD] Byte NN', '[BOLD] (Gridach, 2017)']
The byte NN model is better at finding cell type or lines, organisms or species, and protein or genes than the word NN model. We examine the fact that the word NN model has an F1 score 10% higher than that of other models for small molecules. Although a large number (55%) of the entities in the Bio-ID dataset are protein and genes, we find that the proportion of small molecules mistaken for protein or genes is higher than that of other entities mistaken for protein or genes. Looking at overall sequences of words may be necessary for more accurate identification of small molecules. Because the annotation of JNLPBA and GENETAG were explicitly constrained to words, we believe they do not serve as useful bases for our exploration of byte-level models. Our initial results on these datasets indicate that fully end-to-end byte-level models may be more suitable for entities whose spans do not align with word spans.