The larger an encoding dimension in NLP the better? Is the longer encoding array (word2vec or any other kind) always more precise than the shorter (not regarding the computational cost, only the performance)?
No, it isn't that way. From my personal experience, I have built custom word2vec's to some datasets and observed that at times, shorter vectors did better than longer ones. I think you can try shorters length vectors for smaller datasets. I cannot tell any metric in specific for comparison of dataset size and word2vec length though.