# 1D convolutions, word2vec, and $n$-grams

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.

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

Question 2 Also for each of the $$2$$-grams and $$3$$-grams, would you try to learn some large number of $$2 \times 2$$ filters and $$3 \times 3$$ filters so that you can convolve it with the word2vec embedding? How do you learn these filters?

• Maybe you should post your Question 2 as a separate question. One distinct question per post is usually better. – Philip Raeisghasem Apr 13 at 22:05