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]
]  

# [barack,action1,china]   "to china", "went ... yesterday"
# [action1,activity,moveto]
# [action1,time,past]

# [he,action2, grandhyattbeijing]
# [action2,activity,live_in]
# [action2,time,current]

# TODO:
#sentencetable=[
#  [["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)