OpenIE-standalone

The field of Open Information Extraction has been trying to do that in a generic way for a long time, but the results are still far from good. A few references: OpenIE [1] Graphene [2] MinIE [3]. If you already have a Knowledge Graph (KG) and want to populate its instances from documents, that's called KG Population, and Knowledge-net [4] is a good reference. Relation Extraction is another interesting approach if you know which kind of relations you're interested in, OpenNRE [5] a good example. [1] https://github.com/dair-iitd/OpenIE-standalone [2] https://github.com/Lambda-3/Graphene [3] https://github.com/uma-pi1/minie [4] https://github.com/diffbot/knowledge-net [5] https://github.com/thunlp/OpenNRE

not the old-school NLP that looks at grammar and such things to (surprisingly) find that "X is a Y ." and "is X a Y ?" are not the same sequence of tokens I do that too. It works great - for easy cases. But it fails very quickly on just normal texts. So something like Stanford's CoreNLP Open Information Extraction splits "History is full of such prejudices paraded as iron laws that men are superior to women; that the white races are superior to the colored" into two claims[1]. There's no useful dependency between the two clauses. OpenIE 5[2] (no relationship with the Stanford project) generally outperforms CoreNLP for open information extraction. In this case I'm doubtful it would do any better. Ironically, OpenIE is now run AllenAI, and has exactly this problem! Even worse, it has determined that "No white person" is a synonym for "white