Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set.
['Method', 'T', 'A', 'AT']
As defined in Jimeno-Yepes et al. Terms (T) are the most difficult to disambiguate, while abbreviations seem to be the easiest set. SVM with word embeddings and unigrams performs the best on all the categories. LSTM seems to be better for terms compared to SVM when unigrams and word embeddings are used separately. This different seems to be less clear for abbreviations and ATs. SVM with word embeddings seems to perform less well for the AT group in comparison to the performance in other categories with other methods.