I have a dataset of sentences in a non english language like :
word1 word2 word3 word62
word5 word1 word2
Now i want to turn each variable length sentence to a fixed size vector to give it to my model, and i want all the words in the sentences to have effect on the output
I thought maybe i can use an algorithm like word2vec and turn each word into a fixed size vector, and add all of them to represent the sentence, is this a meaningful approach? is this better than adding the hot one vectors of the words to represent the sentence? is there a better approach than these two?
EDIT1: basically i have a dataset of random variable length sentences and i want to embed them the best way possible, meaning keeping as much information as possible in the resulting embedded vectors (which all have the same size)