Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set.
['Method', '2 senses', '3 senses']
The NLM WSD data set has 34 ambiguous words annotated with 2 senses and 6 with 3 senses. The word cold is the only word with 4 senses annotated. We find that words with 2 senses are typically disambiguated with higher accuracy, while 3 senses seem to be disambiguated with lower accuracy. There is a top of 100 examples for each ambiguous word, so if 3 senses appear, there is less training data per ambiguous word sense.