Example NL extractor 2

intxt="""Barack Obama went to China yesterday. He lives in Grand Hyatt Beijing. This is a superb hotel."""

nertable=[

 [["Barack","Obama"],"Barack Obama","ner_noun","http://en.wikipedia.org/wiki/Barack_Obama","person"],
 [["China"],"China","ner_noun","http://en.wikipedia.org/wiki/China","country"],
 [["Grand","Hyatt","Beijing"],"Grand Hyatt Beijing","ner_noun","https://en.wikipedia.org/wiki/Grand_Hyatt_Beijing","company"]

]

postable=[

 [["went"],"go","verb","http://conceptnet5.media.mit.edu/data/5.3/c/en/go","past"],
 [["to"],"to","preposition","http://conceptnet5.media.mit.edu/data/5.3/c/en/to",None],
 [["yesterday"],"yesterday","adverb","http://conceptnet5.media.mit.edu/data/5.3/c/en/yesterday",None],
 [["he"],"he","pronoun","http://conceptnet5.media.mit.edu/data/5.3/c/en/this",None],
 [["lives"],"live","verb","http://conceptnet5.media.mit.edu/web/c/en/live",None],
 [["in"],"in","preposition","http://conceptnet5.media.mit.edu/web/c/en/in",None],
 [["this"],"this","pronoun","http://conceptnet5.media.mit.edu/web/c/en/this",None],
 [["is"],"be","verb","http://conceptnet5.media.mit.edu/web/c/en/type/v/identify_as_belonging_to_a_certain_type",None],
 [["superb"],"superb","adjective","http://conceptnet5.media.mit.edu/web/c/en/superb",None],
 [["hotel"],"hotel","noun","http://conceptnet5.media.mit.edu/web/c/en/hotel",None]

]

1. [barack,action1,china] "to china", "went ... yesterday"
2. [action1,activity,moveto]
3. [action1,time,past]

1. [he,action2, grandhyattbeijing]
2. [action2,activity,live_in]
3. [action2,time,current]

1. TODO:
2. sentencetable=[
3. [["noun","verb","noun"],0,1,2]

def main(txt):

 splitted=split_text(txt)
 print("splitted:")
 print(splitted)
 nerred=ner_text(splitted)
 print("nerred:")
 print(nerred)
 posed=pos_text(nerred)
 print("posed:")
 print(posed)
 pretty_print(posed)
 rdf=simple_rdf(posed)
 print("rdf:")
 print(rdf)
 pretty_print(rdf)
 

def ner_text(slst):

 rlst=[]
 for sent in slst:
   srlst=[]
   i=0
   while i<len(sent):
     tmp=sent_has_name_at(sent,i)
     if tmp:
       srlst.append(tmp[0])
       i=tmp[1]
     else:
       srlst.append(sent[i])
     i+=1  
   rlst.append(srlst)
 return rlst

def sent_has_name_at(sent,i):

 if not sent: return 0
 if i>=len(sent): return 0
 for known in nertable:
   phrase=known[0]
   j=0
   while j<len(phrase):
     if i+j>=len(sent): break
     if sent[i+j]!=phrase[j]:
       break
     j+=1
   if j==len(phrase):
     res=[known,i+len(phrase)-1]
     return res

def pos_text(slst):

 rlst=[]
 for sent in slst:
   srlst=[]
   i=0
   while i<len(sent):
     if type(sent[i])==type([0]): 
       srlst.append(sent[i])
       i+=1
       continue
     tmp=sent_has_pos_at(sent,i)
     if tmp:
       srlst.append(tmp[0])
       i=tmp[1]
     else:
       srlst.append(sent[i])
     i+=1  
   rlst.append(srlst)
 return rlst

def sent_has_pos_at(sent,i):

 if not sent: return 0
 if i>=len(sent): return 0
 for known in postable:
   phrase=known[0]
   j=0
   while j<len(phrase):
     if i+j>=len(sent): break
     if sent[i+j]!=phrase[j] and sent[i+j].lower()!=phrase[j]:
       break
     j+=1
   if j==len(phrase):
     res=[known,i+len(phrase)-1]
     return res

def split_text(txt):

 sentlst=txt.replace(","," ").split(".")
 wlst=[]
 for s in sentlst:
   if not s: continue
   sp=s.replace("."," ").replace("\n"," ").split(" ")
   tmp=[]
   for w in sp:
     w1=w.strip()
     if w1: tmp.append(w1)      
   wlst.append(tmp)
 return wlst

def pretty_print(sentlst):

 for sent in sentlst:
   print("sentence: ")
   if type(sent)==type([1]):
     for phrase in sent:
       print("  "+str(phrase)) 

def simple_rdf(sentlst):

 done=[]
 prevsent=None
 for sent in sentlst:
   ns=simple_rdf_sentence(sent,prevsent)
   done.append(ns)
   prevsent=sent
 return done    

def simple_rdf_sentence(sent,prevsent):

 verbs=[]
 nouns=[]
 for phrase in sent:
   if type(phrase)!=type([1]): continue
   if phrase[2]=="verb":
     verbs.append(phrase[3])
   elif phrase[2] in ["ner_noun","noun"]:
     nouns.append(phrase[3])
   elif phrase[2] in ["pronoun"]:
     candidates=get_candidate_nouns(prevsent)
     if candidates:
       nouns.append(candidates)  
 if verbs and len(nouns)>1:
   rdf=[nouns[0],verbs[0],nouns[1]]      
 else:
   rdf=None    
 return rdf

def get_candidate_nouns(sent):

 lst=[]
 for phrase in sent:
   if phrase[2] in ["ner_noun","noun"]:
     lst.append(phrase[3])
 return lst
 

main(intxt)