def stateorprovince(self, slot_type, evidence_slot_type):
current_output = self.query_answer.output[slot_type]
city = None
# find query's city answer.
for line_output in self.query_answer.output[evidence_slot_type]:
if line_output.slot_filler:
city = line_output
if city is None:
return current_output
# infer province by city
province = ''
evidence = '' # evidence is a LineOutput object
city_slot_filler = city.slot_filler
city_slot_filler = jianfan.ftoj(city_slot_filler)
for r in [u'区', u'县', u'市']:
city_slot_filler = city_slot_filler.replace(r, '')
for p in self.china_province_city:
if province:
break
if p['type'] == 0:
if city_slot_filler in [item['name'] for item in p['sub']]:
province = p['name']
evidence = city
break
else:
for c in p['sub']:
if city_slot_filler in [item['name'] for item in c['sub']]:
province = p['name']
evidence = city
break
# if inference fails, return original answer
if not province:
return current_output
# search provenance
found_doc_path = search(province + city_slot_filler, self.searcher,
self.analyzer, 50)
if not found_doc_path:
return current_output
evidence_doc_path = found_doc_path[0]
# add additional doc to source_doc for visualization
doc_id = evidence_doc_path.split('/')[-1].strip()
doc = io.open(evidence_doc_path, 'r', -1, 'utf-8').read()
self.sf_object.query_docs[doc_id] = doc
wp_beg = doc.find(province + city_slot_filler)
wp_end = wp_beg + len(province + city_slot_filler) - 1
sp_beg = wp_beg + doc[wp_beg:wp_end + 1].find(province)
sp_end = sp_beg + len(province) - 1
l = LineOutput()
l.slot_type = slot_type
l.run_id = self.query_answer.run_id
p = Provenance()
p.doc_id = doc_id
p.beg = wp_beg
p.end = wp_end
p.text = province + city_slot_filler
l.wide_provenance = [p]
evidence.wide_provenance[0].inference = True
l.wide_provenance += evidence.wide_provenance # evidence is a LineOutput object
l.slot_filler = province
p = Provenance()
p.doc_id = doc_id
p.beg = sp_beg
p.end = sp_end
p.text = province
l.slot_filler_prov = [p]
l.confidence_score = 1
return current_output + [l]
def country(self, slot_type, evidence_slot_type):
current_output = self.query_answer.output[slot_type]
province = None
# find query's province and city answer.
for line_output in self.query_answer.output[evidence_slot_type]:
if line_output.slot_filler:
province = line_output
if province is None:
return current_output
# infer country by province
country = ''
evidence = '' # evidence is a LineOutput object
state_slot_filler = jianfan.ftoj(province.slot_filler)
for c in self.world_coutry_province:
if state_slot_filler in self.world_coutry_province[c]:
country = c
evidence = province
break
# if inference fails, return original answer
if not country:
return current_output
# search provenance
found_doc_path = search(country + state_slot_filler,
self.sf_object.lucene_searcher,
self.sf_object.lucene_analyzer, 50)
if not found_doc_path:
return current_output
evidence_doc_path = found_doc_path[0]
# add additional doc to source_doc for visualization
doc_id = evidence_doc_path.split('/')[-1].strip()
doc = io.open(evidence_doc_path, 'r', -1, 'utf-8').read()
self.sf_object.query_docs[doc_id] = doc
wp_beg = doc.find(country + state_slot_filler)
wp_end = wp_beg + len(country + state_slot_filler) - 1
sp_beg = wp_beg + doc[wp_beg:wp_end + 1].find(country)
sp_end = sp_beg + len(country) - 1
l = LineOutput()
l.slot_type = slot_type
l.run_id = self.query_answer.run_id
p = Provenance()
p.doc_id = doc_id
p.beg = wp_beg
p.end = wp_end
p.text = country + state_slot_filler
l.wide_provenance = [p]
evidence.wide_provenance[0].inference = True
l.wide_provenance += evidence.wide_provenance # evidence is a LineOutput object
l.slot_filler = country
p = Provenance()
p.doc_id = doc_id
p.beg = sp_beg
p.end = sp_end
p.text = country
l.slot_filler_prov = [p]
l.confidence_score = 1
# if province is 台湾, coutry should also add 台湾
if u'台湾' in jianfan.ftoj(province.slot_filler):
return current_output + [l, province]
return current_output + [l]
def create_line_output(self, e, slot_filler, slot_filler_index, slot_type, combined_slot_filler=False, confidence_score=1):
doc_id = e.doc_id
parse_result = e.parse_result
l = LineOutput()
l.slot_type = slot_type
evidence_offset_beg = self.sf_object.cleaned_docs[doc_id].find(''.join(parse_result['text']))
w_p = Provenance()
w_p.doc_id = doc_id
cleaned_doc_beg = int(parse_result['words'][0][1]['CharacterOffsetBegin'])
cleaned_doc_end = int(parse_result['words'][-1][1]['CharacterOffsetEnd'])-1
if cleaned_doc_beg == 0:
cleaned_doc_beg += evidence_offset_beg
cleaned_doc_end += evidence_offset_beg
w_p.beg = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_beg]
w_p.end = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_end]
w_p.text = e.sent_text
w_p.trigger = e.trigger
l.wide_provenance = [w_p]
l.slot_filler = slot_filler
sf_p = Provenance()
sf_p.doc_id = doc_id
if combined_slot_filler:
cleaned_doc_beg = ''.join(parse_result['text']).find(slot_filler)
cleaned_doc_end = cleaned_doc_beg+len(slot_filler)-1
else:
# here node index in dependency graph need -1 because it start from root which indexed 0
cleaned_doc_beg = int(parse_result['words'][slot_filler_index-1][1]['CharacterOffsetBegin'])
cleaned_doc_end = int(parse_result['words'][slot_filler_index-1][1]['CharacterOffsetEnd'])-1
if int(parse_result['words'][0][1]['CharacterOffsetBegin']) == 0 or combined_slot_filler:
cleaned_doc_beg += evidence_offset_beg
cleaned_doc_end += evidence_offset_beg
sf_p.beg = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_beg]
sf_p.end = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_end]
sf_p.text = slot_filler
l.slot_filler_prov = [sf_p]
l.confidence_score = confidence_score
return l
def stateorprovince(self, slot_type, evidence_slot_type):
current_output = self.query_answer.output[slot_type]
city = None
# find query's city answer.
for line_output in self.query_answer.output[evidence_slot_type]:
if line_output.slot_filler:
city = line_output
if city is None:
return current_output
# infer province by city
province = ''
evidence = '' # evidence is a LineOutput object
city_slot_filler = city.slot_filler
city_slot_filler = jianfan.ftoj(city_slot_filler)
for r in [u'区', u'县', u'市']:
city_slot_filler = city_slot_filler.replace(r, '')
for p in self.china_province_city:
if province:
break
if p['type'] == 0:
if city_slot_filler in [item['name'] for item in p['sub']]:
province = p['name']
evidence = city
break
else:
for c in p['sub']:
if city_slot_filler in [item['name'] for item in c['sub']]:
province = p['name']
evidence = city
break
# if inference fails, return original answer
if not province:
return current_output
# search provenance
found_doc_path = search(province + city_slot_filler, self.searcher, self.analyzer, 50)
if not found_doc_path:
return current_output
evidence_doc_path = found_doc_path[0]
# add additional doc to source_doc for visualization
doc_id = evidence_doc_path.split('/')[-1].strip()
doc = io.open(evidence_doc_path, 'r', -1, 'utf-8').read()
self.sf_object.query_docs[doc_id] = doc
wp_beg = doc.find(province + city_slot_filler)
wp_end = wp_beg + len(province + city_slot_filler) - 1
sp_beg = wp_beg + doc[wp_beg:wp_end+1].find(province)
sp_end = sp_beg + len(province) - 1
l = LineOutput()
l.slot_type = slot_type
l.run_id = self.query_answer.run_id
p = Provenance()
p.doc_id = doc_id
p.beg = wp_beg
p.end = wp_end
p.text = province+city_slot_filler
l.wide_provenance = [p]
evidence.wide_provenance[0].inference = True
l.wide_provenance += evidence.wide_provenance # evidence is a LineOutput object
l.slot_filler = province
p = Provenance()
p.doc_id = doc_id
p.beg = sp_beg
p.end = sp_end
p.text = province
l.slot_filler_prov = [p]
l.confidence_score = 1
return current_output+[l]
def country(self, slot_type, evidence_slot_type):
current_output = self.query_answer.output[slot_type]
province = None
# find query's province and city answer.
for line_output in self.query_answer.output[evidence_slot_type]:
if line_output.slot_filler:
province = line_output
if province is None:
return current_output
# infer country by province
country = ''
evidence = '' # evidence is a LineOutput object
state_slot_filler = jianfan.ftoj(province.slot_filler)
for c in self.world_coutry_province:
if state_slot_filler in self.world_coutry_province[c]:
country = c
evidence = province
break
# if inference fails, return original answer
if not country:
return current_output
# search provenance
found_doc_path = search(country + state_slot_filler,
self.sf_object.lucene_searcher, self.sf_object.lucene_analyzer, 50)
if not found_doc_path:
return current_output
evidence_doc_path = found_doc_path[0]
# add additional doc to source_doc for visualization
doc_id = evidence_doc_path.split('/')[-1].strip()
doc = io.open(evidence_doc_path, 'r', -1, 'utf-8').read()
self.sf_object.query_docs[doc_id] = doc
wp_beg = doc.find(country + state_slot_filler)
wp_end = wp_beg + len(country + state_slot_filler) - 1
sp_beg = wp_beg + doc[wp_beg:wp_end+1].find(country)
sp_end = sp_beg + len(country) - 1
l = LineOutput()
l.slot_type = slot_type
l.run_id = self.query_answer.run_id
p = Provenance()
p.doc_id = doc_id
p.beg = wp_beg
p.end = wp_end
p.text = country+state_slot_filler
l.wide_provenance = [p]
evidence.wide_provenance[0].inference = True
l.wide_provenance += evidence.wide_provenance # evidence is a LineOutput object
l.slot_filler = country
p = Provenance()
p.doc_id = doc_id
p.beg = sp_beg
p.end = sp_end
p.text = country
l.slot_filler_prov = [p]
l.confidence_score = 1
# if province is 台湾, coutry should also add 台湾
if u'台湾' in jianfan.ftoj(province.slot_filler):
return current_output+[l, province]
return current_output+[l]
def create_line_output(self,
e,
slot_filler,
slot_filler_index,
slot_type,
combined_slot_filler=False,
confidence_score=1):
doc_id = e.doc_id
parse_result = e.parse_result
l = LineOutput()
l.slot_type = slot_type
evidence_offset_beg = self.sf_object.cleaned_docs[doc_id].find(''.join(
parse_result['text']))
w_p = Provenance()
w_p.doc_id = doc_id
cleaned_doc_beg = int(
parse_result['words'][0][1]['CharacterOffsetBegin'])
cleaned_doc_end = int(
parse_result['words'][-1][1]['CharacterOffsetEnd']) - 1
if cleaned_doc_beg == 0:
cleaned_doc_beg += evidence_offset_beg
cleaned_doc_end += evidence_offset_beg
w_p.beg = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_beg]
w_p.end = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_end]
w_p.text = e.sent_text
w_p.trigger = e.trigger
l.wide_provenance = [w_p]
l.slot_filler = slot_filler
sf_p = Provenance()
sf_p.doc_id = doc_id
if combined_slot_filler:
cleaned_doc_beg = ''.join(parse_result['text']).find(slot_filler)
cleaned_doc_end = cleaned_doc_beg + len(slot_filler) - 1
else:
# here node index in dependency graph need -1 because it start from root which indexed 0
cleaned_doc_beg = int(
parse_result['words'][slot_filler_index -
1][1]['CharacterOffsetBegin'])
cleaned_doc_end = int(
parse_result['words'][slot_filler_index -
1][1]['CharacterOffsetEnd']) - 1
if int(parse_result['words'][0][1]
['CharacterOffsetBegin']) == 0 or combined_slot_filler:
cleaned_doc_beg += evidence_offset_beg
cleaned_doc_end += evidence_offset_beg
sf_p.beg = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_beg]
sf_p.end = self.sf_object.doc_mapping_table[doc_id][cleaned_doc_end]
sf_p.text = slot_filler
l.slot_filler_prov = [sf_p]
l.confidence_score = confidence_score
return l
