Suppose we are using word2vec and have embeddings of individual words $w_1, \dots, w_{10}$. Let's say we wanted to analyze $2$ grams or $3$ grams.

Why would adding all the possible embeddings, $\binom{10}{2}$ or $\binom{10}{3}$, be "worse" than using 1D-convolutions?