def post(self):
args = self.reqparse.parse_args()
text = args['text']
country_filter = ["SYR", "IRQ"]
out = utilities.talk_to_mitie(text)
for i in out['entities']:
print i['text'],
if i['text'] in country_names:
print " (Country/blacklist. Skipping...)"
else:
# put this in query_geonames?
searchterm = re.sub(
r"Governorate|District|Subdistrict|Airport", "",
i['text']).strip()
searchterm = re.sub("Dar 'a", "Dar'a", searchterm)
t = query_geonames(searchterm, country_filter)
loc = pick_best_result(t, i['text'])
# loc is a nice format for debugging and looks like [35.13179, 36.75783, 'searchterm', u'matchname', u'feature_class', u'country_code3']:
formatted_loc = {
"lat": loc[0],
"lon": loc[1],
"seachterm": loc[2],
"placename": loc[3],
"countrycode": loc[5]
}
return jsonify(formatted_loc)
def post(self):
args = self.reqparse.parse_args()
text = args['text']
country_filter = ["SYR", "IRQ"]
out = utilities.talk_to_mitie(text)
for i in out['entities']:
print i['text'],
if i['text'] in country_names:
print " (Country/blacklist. Skipping...)"
else:
# put this in query_geonames?
searchterm = re.sub(r"Governorate|District|Subdistrict|Airport",
"", i['text']).strip()
searchterm = re.sub("Dar 'a", "Dar'a", searchterm)
t = query_geonames(searchterm, country_filter)
loc = pick_best_result(t, i['text'])
# loc is a nice format for debugging and looks like [35.13179, 36.75783, 'searchterm', u'matchname', u'feature_class', u'country_code3']:
formatted_loc = {"lat": loc[0], "lon": loc[1],
"seachterm": loc[2], "placename": loc[3],
"countrycode": loc[5]}
return jsonify(formatted_loc)
def process(self, text):
out = utilities.talk_to_mitie(text, ner_model)
places = []
miscs = []
for i in out['entities']:
if i['tag'] == "LOCATION" or i['tag'] == "location":
places.append(i['text'])
if i['tag'] == "MISC" or i['tag'] == "misc":
miscs.append(i['text'])
loc_list = [re.sub(" ", "_", element) for element in places]
locs = [x for x in loc_list if x in vocab_set]
if locs:
locs_word_vec = [prebuilt[word] for word in locs]
locs_vec = numpy.array(locs_word_vec)
weights = numpy.dot(index,
matutils.unitvec(locs_vec.mean(axis=0)).T).T
ranks = weights.argsort()[::-1]
try:
return idx_country_mapping[ranks[0]]
except:
return []
else:
misc_list = [re.sub(" ", "_", element) for element in miscs]
misc = [x for x in misc_list if x in vocab_set]
misc_word_vec = [prebuilt[word] for word in misc]
misc_vec = numpy.array(misc_word_vec)
weights = numpy.dot(index,
matutils.unitvec(misc_vec.mean(axis=0)).T).T
ranks = weights.argsort()[::-1]
try:
return idx_country_mapping[ranks[0]]
except:
return []
def process(self, text):
out = utilities.talk_to_mitie(text, ner_model)
places = []
miscs = []
for i in out['entities']:
if i['tag'] == "LOCATION" or i['tag'] == "location":
places.append(i['text'])
if i['tag'] == "MISC" or i['tag'] == "misc":
miscs.append(i['text'])
loc_list = [re.sub(" ", "_", element) for element in places]
locs = [x for x in loc_list if x in vocab_set]
if locs:
locs_word_vec = [prebuilt[word] for word in locs]
locs_vec = numpy.array(locs_word_vec)
weights = numpy.dot(index,
matutils.unitvec(locs_vec.mean(axis=0)).T).T
ranks = weights.argsort()[::-1]
try:
return idx_country_mapping[ranks[0]]
except:
return []
else:
misc_list = [re.sub(" ", "_", element) for element in miscs]
misc = [x for x in misc_list if x in vocab_set]
misc_word_vec = [prebuilt[word] for word in misc]
misc_vec = numpy.array(misc_word_vec)
weights = numpy.dot(index,
matutils.unitvec(misc_vec.mean(axis=0)).T).T
ranks = weights.argsort()[::-1]
try:
return idx_country_mapping[ranks[0]]
except:
return []
