Given the task definition and input, reply with output. In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

We use two different unsupervised approaches for word sense disambiguation.  The second, called `dense model', represents synsets and contexts in a dense, low-dimensional space by averaging word embeddings. In the synset embeddings model approach, we follow SenseGram BIBREF14 and apply it to the synsets induced from a graph of synonyms.  We observe that the SenseGram-based approach for word sense disambiguation yields substantially better results in every case (Table TABREF25 ). The primary reason for that is the implicit handling of similar words due to the averaging of dense word vectors for semantically related words. 

Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?