Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set.
['Method', '2 senses', '3 senses']
We have grouped ambiguous terms according to several criteria. Results are shown for words with 2 senses (189 words) and 3 senses (12 words). Ambiguous words with 4 and 5 senses appear only one in the data set. Macro average shows that words with 2 senses are easier to disambiguate and that words with 3 senses are slightly more complicated. Methods relying solely on word embeddings as features seem to have a larger drop in performance between 2 word senses and 3 word senses.