'When applying word2vec, should I standardize the cosine values within each year, when comparing them across years?
I'm a researcher, and I'm trying to apply NPL to understand the temporal changes of the meaning of some words. So far I have obtained the trained embeddings (word2vec, sgn) of several years with identical parameters in the training. For example, if I want to test the change of cosine similarity of word A and word B over 5 years, should I just compute them and plot the cosine values?
The reason I'm asking this is that I found the overall cosine values (mean of all possible pairs within that year) differ across the 5 years. **For example, 1990:0.21, 1991:0.19, 1992:0.31, 1993:0.22, 1994:0.31. Does it mean in some years, all words are more similar to each other than other years??
Base on my limited understanding, I think the vectors are odds in logistic functions, so they shouldn't be significantly affected by the size of the corpus? Is it necessary for me to standardize the cosine values (of all pairs within each year) so I can compare the relative ranking change across years? Or just trust the raw cosine values and compare them across years?
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