# Why would adding all the possible embeddings be “worse” than using 1D-convolutions?

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?