3 ms·
I agree, and I have an idea for this dimensionality reduction which makes the original unsupervized word vectors interpretable. it boggles my mind I haven't se
by DoctorOetker 7y ago
I agree, and I have an idea for this dimensionality reduction which makes the original unsupervized word vectors interpretable.
it boggles my mind I haven't seen anyone implement my idea.
- gattilorenz 7y agoWell, it might be because it's hard to read your mind from here.
- DoctorOetker 7y agoit's so obvious one wouldn't have to read my mind, it's all implicit in the king-man+woman=queen type of relations... if you really ask a second time, fuck it, im not in ML sector, perhaps I just give away the idea...
- DoctorOetker 7y agono, that wouldn't be mindboggling what's mindboggling to me is that I haven't seen anyone else come up with the idea independently.
- aglionby 7y agoSVD has been used for dimensionality reduction of co-occurrence matrices for ages [1], but the resulting word embeddings aren't as performant as those of word2vec/etc. The same is probably true of using PCA. Word2vec's popularity is the result of people valuing performance (i.e. accuracy) more than interpretability. [1] https://dl.acm.org/citation.cfm?id=148132 https://dl.acm.org/citation.cfm?id=148132