I have 100 sentences that I want to cluster based on similarity. I've used doc2vec to vectorize the sentences into 20 dimensional vectors and applied kmeans to cluster them. I haven't got the desired results yet.
I've read that doc2vec performs well only on large datasets. I want to know if increasing the length of each data sample, would compensate for the low number of samples, and help the model train better?
For example, if my sentences are originally "making coffee", "making tea", "playing with dogs", would changing them to "making coffee requires a cup of milk and some coffee powder", "making tea requires boiling water and some tea leaves" (supplement each document with more information) help in getting better results? Would the model understand the context better?