|
|
|
|
|
|
by tom_mellior
2935 days ago
|
|
|
> not mistaking questions for assertions of fact is basically claim verification. That's pretty much beyond the reach of NLP systems at the moment. Ah, OK. I guess you are one of those people for whom NLP is only the newfangled statistical stuff, 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. > Trying to understand the context of sentences might be possible. I didn't say they must understand the context. I said that if they don't understand it, they shouldn't choose a substring out of that sentence and claim that it is an assertion of fact on its own. |
|
|
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 person"! That should be well within the state of the art to avoid.
But generally, I'm not saying it is correct: I'm saying it's hard.
[1] http://corenlp.run/
[2] https://github.com/dair-iitd/OpenIE-standalone
[3] http://openie.allenai.org/search?arg1=White&rel=superior&arg...