class LanguageUnderstanding(object):
def __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
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
class LanguageUnderstanding(object):
def __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
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
class LanguageUnderstanding(object):
def __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
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
class LanguageUnderstanding(object):
def __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
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 __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
def __init__(self):
self.__predictor = DialogueActTypePredictor()
self.__extractor = NamedEntityExtractor()
