Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Table 3: Macro and micro average disambiguation results NLM WSD
['Features/ML', 'NB', 'SVM', 'KNN1', 'KNN3', 'KNN5']
Unigrams is as well a strong feature representation, on the other hand combining different feature sets do not improve the performance over unigrams. NB is significantly better than SVM, which is another difference compared to the MSH WSD data set. All the results are better compared to the maximum frequency sense baseline (macro average: 82.63 and micro average: 82.31).