Deserved. word2vec is a beautiful peice of science. The hypothesis behind it was so amazing. I kinda feel like everything else in LLMs (including the attention mechanism itself) is just engineering around the representation learning breakthrough that word2vec demonstrated
And with applications far outside the original scope, which I think is one of the hallmarks of foundational discoveries. As soon as you have that one puzzle piece a whole lot of other stuff comes into focus.
Well-deserved. Even though more complex language models won in the end, his stripping down of word embedding training from RNNs/convolution models to a simple linear model was the thing that was needed for compute at the time and completely dominated the field for five years or so. I do miss the times a little where you could train a cutting-edge model with a reasonable CPU (for training embeddings with word2vec) and a single GPU (some BiLSTM or transformers before BERT).
If I remember correctly, I read somewhere he initially had a hard time getting the word2vec paper accepted at a conference.
The idea that a word is defined by the company it keeps was decades old. Mikolov’s insight was that you could learn it at scale with a very simple model, and that meaning would fall out as geometry. Obvious in hindsight, foundational in practice.
Not to downplay his accomplishments (I have sung my praises in another comment), but even vector models were already around and somewhat popular before. Much of it started in Information Retrieval with things like TF-IDF vectors and later PPMI matrices that were converted to small, dense matrices with SVD. Of course, later Yoshua Bengio et al. introduced neural language models for training embeddings.
There is even a paper by Levy & Goldberg from 2014 that shows that skip-gram with negative sampling implicitly factorizes a PPMI word-context matrix.
But Mikolov's formulation is very nice in that it is a simple linear model that allows for a very simple Hogwild optimization that worked very well/fast on multi-core CPUs of the time.
I still in awe how could someone understand well in math, is it pure nature or nurture. Could any average possible to grasp the beauty of math, starting from high school level math understanding.
If I remember correctly, I read somewhere he initially had a hard time getting the word2vec paper accepted at a conference.
There is even a paper by Levy & Goldberg from 2014 that shows that skip-gram with negative sampling implicitly factorizes a PPMI word-context matrix.
But Mikolov's formulation is very nice in that it is a simple linear model that allows for a very simple Hogwild optimization that worked very well/fast on multi-core CPUs of the time.