3 ms·

I think you might get better results in the first stage using the dependency parse from CoreNLP - rather than the phrasal parse. Online demo at http://corenlp.r

I think you might get better results in the first stage using the dependency parse from CoreNLP - rather than the phrasal parse. Online demo at http://corenlp.run http://corenlp.run If you're willing to drop CoreNLP there's also https://demos.explosion.ai/displacy/ https://demos.explosion.ai/displacy/ that's worth checking out.

alanlit 10y ago
Nice work by the O.P. Amusingly a year or so ago I took the Stanford Dependency parser and fed its output tree into a Prolog system to try to pull out the semantics. It was used to analyze business news (getting at the who's, what's and why's). The easiest approach was to wrap a very simple DSL around Prolog (which, BTW, Prolog is great at). Then in the DSL (which still retained logical variables and backtracking) you could write things like: %% Simple statements -- root is an announcement word whose subject and object tell the story. %% 'IBM announced a new computer today' announce(Who, About, What) ==> s+root(['announc', 'releas', 'introduc','launch', 'unveil', 'reveal', 'agre']), #Dep1, subject(Who), Dep1 >> object(About, What). %% 'IBM has announced a partnership ...' is caught by the above. But 'IBM has entered into a partnership ...' needs %% a little more work announce(Who, About, announcement) ==> s+root(['enter']), #Obj, subject(Who), Obj >> prep_pobj_chain(PPC), {PPC = [Prep|About]}. I think a Prolog-based query planner as a front end to Sparql on Wikidata could be quite interesting. Alanl
- alanlit 10y ago
  Bah -- try again so it is readable !! %% Simple statements -- root is an announcement word whose subject and object tell the story. %% 'IBM announced a new computer today' announce(Who, About, What) ==> s+root(['announc', 'releas', 'introduc','launch', 'unveil', 'reveal', agre']), #Dep1, subject(Who), Dep1 >> object(About, What). %% 'IBM has announced a partnership ...' is caught by the above. But 'IBM has entered into a partnership ...' needs %% a little more work announce(Who, About, announcement) ==> s+root(['enter']), #Obj, subject(Who), Obj >> prep_pobj_chain(PPC), {PPC = [Prep|About]}.

alanlit 10y ago

Nice work by the O.P. Amusingly a year or so ago I took the Stanford Dependency parser and fed its output tree into a Prolog system to try to pull out the semantics. It was used to analyze business news (getting at the who's, what's and why's). The easiest approach was to wrap a very simple DSL around Prolog (which, BTW, Prolog is great at). Then in the DSL (which still retained logical variables and backtracking) you could write things like: %% Simple statements -- root is an announcement word whose subject and object tell the story. %% 'IBM announced a new computer today' announce(Who, About, What) ==> s+root(['announc', 'releas', 'introduc','launch', 'unveil', 'reveal', 'agre']), #Dep1, subject(Who), Dep1 >> object(About, What). %% 'IBM has announced a partnership ...' is caught by the above. But 'IBM has entered into a partnership ...' needs %% a little more work announce(Who, About, announcement) ==> s+root(['enter']), #Obj, subject(Who), Obj >> prep_pobj_chain(PPC), {PPC = [Prep|About]}. I think a Prolog-based query planner as a front end to Sparql on Wikidata could be quite interesting. Alanl

alanlit 10y ago
Bah -- try again so it is readable !! %% Simple statements -- root is an announcement word whose subject and object tell the story. %% 'IBM announced a new computer today' announce(Who, About, What) ==> s+root(['announc', 'releas', 'introduc','launch', 'unveil', 'reveal', agre']), #Dep1, subject(Who), Dep1 >> object(About, What). %% 'IBM has announced a partnership ...' is caught by the above. But 'IBM has entered into a partnership ...' needs %% a little more work announce(Who, About, announcement) ==> s+root(['enter']), #Obj, subject(Who), Obj >> prep_pobj_chain(PPC), {PPC = [Prep|About]}.