Evaluating Sparse Interpretable Word Embeddings for Biomedical DomainThis research was supported by the Institute for Artificial Intelligence (iai.iut.ac.ir) under a generous grant by Mr. Farhad Rahnema.
Table 1: Word similarity and relatedness results
['[BOLD] Vectors', '[BOLD] SimLex', '[BOLD] Bio-SimLex', '[BOLD] Bio-SimVerb', '[BOLD] UMNSRS-Sim', '[BOLD] UMNSRS-Rel']
One important reason for looking into such comparison is to make sure that the expected interpretability of the sparse methods is achieved at either no or tolerable loss in preserving the similarity and relatedness captured by their original versions. As it can be seen, sparse word vectors obtained by SPOWV have even outperformed the original vectors on several occasions. The sparse vectors obtained by SPINE however, show a slight loss of performance which seems nonetheless tolerable considering the high interpretability it provides, as shown in the next subsection.