Unsupervised word embeddings capture latent knowledge from scientific literature
nature.com
nature.com
Interesting points on future work:
- This was only using abstracts. Using full papers could yield significant improvements.
- Uses word2vec and not Bert / Elmo, so there's likely to be another jump in performance there.
The author emails are at the end of README.md if you still want to ask for a preprint.
It is probably hype, but if that sentence is taken literally it would be huge.
The limits on human innovation have historically been chemical/materials science related rather than a lack of imagination. Anything that allows search to be deployed on things that don't even exist would be ... well, big.
It's not really that hype but it's also neither that novel and results in this type of domain still have to be verified through other means. $foo2vec papers have been doing this for several domains, framed as text retrieval and link prediction / knowledge base completion, for a few years now.
We showed how chemical-application & chemical-property relations are captured by word2vec and GloVe. For instance we found rocket fuels where the chemicals appearing closest to “rocket” while materials used in air bags appeared closest to “air bag”. We were able to filter to chemical names using ChemDataExtractor and further to likely energetic chemicals by obtaining SMILES strings from PubChem and using a classifier to classify them as likely energetics or not.
You can find our work here : https://arxiv.org/pdf/1903.00415.pdf .
Is there anything new methodology wise in the nature version?