def execute(self, sent):
# 対話行為タイプの推定開始
# 4タイプ
# - genre: イタリアンとか
# - location: 新宿とか
# - money: 1万円とか
# - other: その他
features = sent2features_(sent)
#print("送信されたテキストから変換したfeatures:", features)
act_type = self.__predictor.predict([features])
print("featureから予測したact_type:", act_type)
# 対話行為タイプの推定完了
# 属性抽出
# ジャンルに対して文字を抽出。イタリアンとか中華とか
surfaces, features = analyze_morph(sent)
print("surfaces:", surfaces)
print("features:", features)
morphed_sent = [[surfaces[i]] + features[i].split(',')
for i in range(len(surfaces))]
print("morphed_sent", morphed_sent)
features = sent2features(morphed_sent)
named_entity = self.__extractor.extract(features, morphed_sent)
dialogue_act = {'user_act_type': act_type}
# ここで属性を追加
dialogue_act.update(dict(named_entity))
return dialogue_act
def execute(self, sent):
features = sent2features_(sent)
# print("----------features_features--------------")
# print(features)
act_type = self.__predictor.predict([features])
# print("----------act_type--------------")
# print(act_type)
surfaces, features = analyze_morph(sent)
# print("----------surfaces,features--------------")
# print(features)
# print(surfaces)
morphed_sent = [[surfaces[i]] + features[i].split(',')
for i in range(len(surfaces))]
features = sent2features(morphed_sent)
print("----------morphed_sent,features--------------")
print(morphed_sent)
print(features)
named_entity = self.__extractor.extract(features, morphed_sent)
print("----------named_entity--------------")
print(named_entity)
dialogue_act = {'user_act_type': act_type}
if act_type != 'other':
dialogue_act.update(dict(named_entity))
#
# print("----------dialogue_act--------------")
# print(dialogue_act)
return dialogue_act
def execute(self, sent):
features = sent2features_(sent)
act_type = self.__predictor.predict([features])
surfaces, features = analyze_morph(sent)
morphed_sent = [[surfaces[i]] + features[i].split(',') for i in range(len(surfaces))]
features = sent2features(morphed_sent)
named_entity = self.__extractor.extract(features, morphed_sent)
dialogue_act = {'user_act_type': act_type}
dialogue_act.update(dict(named_entity))
return dialogue_act
def execute(self, sent):
features = sent2features_(sent)
act_type = self.__predictor.predict([features])
surfaces, features = analyze_morph(sent)
morphed_sent = [[surfaces[i]] + features[i].split(',')
for i in range(len(surfaces))]
features = sent2features(morphed_sent)
named_entity = self.__extractor.extract(features, morphed_sent)
dialogue_act = {'user_act_type': act_type}
dialogue_act.update(dict(named_entity))
return dialogue_act
f = lambda path: os.path.dirname(path)
root_dir = f(f(f(f(os.path.abspath(sys.argv[0])))))
training_data_dir = os.path.join(root_dir, 'training_data')
training_data = []
for file_name in os.listdir(training_data_dir):
if not file_name.endswith('.pkl'):
continue
file_path = os.path.join(training_data_dir, file_name)
with open(file_path, 'rb') as rf:
tmp_data = pickle.load(rf)
training_data.extend(tmp_data)
random.shuffle(training_data)
train_num = int(len(training_data) * 0.9)
train_sents = training_data[:train_num]
test_sents = training_data[train_num:]
train_x = [sent2features(s) for s in train_sents]
train_y = [sent2labels(s) for s in train_sents]
test_x = [sent2features(s) for s in test_sents]
test_y = [sent2labels(s) for s in test_sents]
extractor = NamedEntityExtractor()
extractor.train(train_x, train_y)
pred_y = [extractor.tagger(xseq) for xseq in test_x]
print(extractor.evaluate(test_y, pred_y))
f = lambda path: os.path.dirname(path)
root_dir = f(f(f(f(__file__))))
training_data_dir = os.path.join(root_dir, "training_data")
training_data = []
for file_name in os.listdir(training_data_dir):
if not file_name.endswith(".pkl"):
continue
file_path = os.path.join(training_data_dir, file_name)
with open(file_path, "rb") as rf:
tmp_data = pickle.load(rf)
training_data.extend(tmp_data)
random.shuffle(training_data)
train_num = int(len(training_data) * 0.9)
train_sents = training_data[:train_num]
test_sents = training_data[train_num:]
train_x = [sent2features(s) for s in train_sents]
train_y = [sent2labels(s) for s in train_sents]
test_x = [sent2features(s) for s in test_sents]
test_y = [sent2labels(s) for s in test_sents]
extractor = NamedEntityExtractor()
extractor.train(train_x, train_y)
pred_y = [extractor.tagger(xseq) for xseq in test_x]
print(extractor.evaluate(test_y, pred_y))
