def Triple_extract(self, path):
TR = Treport(path)
triplelist = {}
Mor_con = [[u"形容詞", u"助動詞", u"接続詞"], [u"連体化", u"並立助詞", u"読点", u"接続助詞"]]
for i in range(1, TR.s.nrows):
#if i>TR.s.nrows/2: break
if i>10: break
print i
noenc = TR.delete_unnecc(i)
#print TR.s.cell_value(i, 2).replace(u"-", u"")
#print noenc
for Sentence_id, perSen in enumerate(noenc.split(u"。")):
# TR.s.cell_value(i, 2)
Lan = Language(perSen)
cabocha_xml = Lan.cabocha_command()
chunkinfo, tokinfo, sentence_tok = Lan.chunk_structured(cabocha_xml)
#triple_perR = []
#id_perR = []
for chunk in chunkinfo:
compnoun_tail_id = -1
for tok_id, tokinfo_mor in enumerate(tokinfo[int(chunk[u"id"])]):
#print tok_id, compnoun_tail_id
if tok_id <= compnoun_tail_id:
continue
sentence_tok_set = sentence_tok[int(chunk[u"id"])]
if tokinfo_mor[0]==u"名詞":
Noun = sentence_tok_set[tok_id]
compnoun_tail_id = tok_id
for tok_id_noun in range(tok_id+1, len(tokinfo[int(chunk[u"id"])])-1):
if tokinfo[int(chunk[u"id"])][tok_id_noun][0]==u"名詞" :
if sentence_tok[int(chunk[u"id"])][tok_id_noun] == u"濃度":
continue
Noun += sentence_tok[int(chunk[u"id"])][tok_id_noun]
compnoun_tail_id = tok_id_noun
else:
break
if compnoun_tail_id+1 == len(tokinfo[int(chunk[u"id"])]):
continue
chunk_id_from = int(chunk[u"id"])
for i_from in reversed(range((int(chunk["id"])+1)*-1, 0)):
if int(chunkinfo[int(chunk[u"id"])+i_from]["link"])==chunk_id_from:
chunk_id_from -= 1
from_tail_tok = tokinfo[int(chunk[u"id"])+i_from][len(tokinfo[int(chunk[u"id"])+i_from])-1]
if from_tail_tok[0] in Mor_con[0] or from_tail_tok[1] in Mor_con[1]:
for sentence_tok_from in reversed(list(sentence_tok[int(chunkinfo[int(chunk[u"id"])+i_from]["id"])])):
Noun = sentence_tok_from + Noun
else:
break
else:
break
if tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][0]==u"助詞" and tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][1]!=u"接続助詞":
Particle = tokinfo[int(chunk[u"id"])][compnoun_tail_id+1][6]
Noun_suru = u""
for tok_id_link, tok_link_mor in enumerate(tokinfo[int(chunk[u"link"])]):
if tok_link_mor[0]==u"名詞" and tok_link_mor[1]!=u"形容動詞語幹":
Noun_suru += sentence_tok[int(chunk[u"link"])][tok_id_link]
continue
if tok_link_mor[0]==u"動詞" or tok_link_mor[0]==u"形容詞" or tok_link_mor[1]==u"形容動詞語幹":
if tok_link_mor[1]!=u"末尾":
Verb = u""
if tok_link_mor[6]==u"する" or tok_link_mor[6]==u"できる":
Verb = Noun_suru+u"する"
else:
Verb = tok_link_mor[6]
Verb_id = int(chunk[u"link"])
#数字以外を削除
if isinstance(TR.s.cell_value(i, 2), float):
id_tuple = (TR.s.cell_value(i, 2), Sentence_id, Verb_id)
else:
if re.search("[0-9]", TR.s.cell_value(i, 2)) is None:
id_tuple = (re.search("\d+[-]*\d+", TR.s.cell_value(i, 1)[re.search("\d+[-]*\d+", TR.s.cell_value(i, 1)).end():]).group(0).replace(u"-", u""), Sentence_id, Verb_id)
else:
id_tuple = (re.search("\d+[-]*\d+", TR.s.cell_value(i, 2)).group(0).replace(u"-", u""), Sentence_id, Verb_id)
if id_tuple not in triplelist.keys():
triplelist[id_tuple] = [(Noun, Particle, Verb)]
else:
triple_tmp = triplelist[id_tuple]
triple_tmp.append((Noun, Particle, Verb))
triplelist[id_tuple] = triple_tmp
#print Noun, Particle, Verb, TR.s.cell_value(i, 2).replace(u"-", u""), Sentence_id, Verb_id
break
return triplelist
