On analyzing the argmax outputs from the inference network of the trained LAP-CINF-UDEC model, we find that 42% of the predicted anchor words are nouns, 39% of them are verbs, and 11% are adjectives, compared to 58%, 33% and 6% respectively for the RAKE extracted keywords for the ROC data.
the inference network learned along-with the LAP-CINF-UDEC model has higher preference for verbs and adjectives compared to the RAKE algorithm.