def listen(self, sentence):
# User和Session管理
pass
# PREPROCESS
stmt = self.preprocessor.process(
Statement(text=sentence, from_user=True))
# NLU
intent, ne = self.nlu.process(stmt)
# DM
status, target, policy = self.dm.process(stmt, None, intent, ne)
# NLG
response = self.nlg.process(status, target, policy)
# 保存CONTEXT信息，持久化
pass
return response
if hit:
newdes.append(idx + 1)
if entity.attrib.get("isresult") == "true":
wordidx.add(idx)
if entity.attrib.get("internode") == "true":
des = newdes
return wordidx
if __name__ == "__main__":
from Chatbot.configs import Configs
cfg = Configs()
cfg.dp_template_path = "./templates/template.xml"
cfg.ltp_seg_model_path = "../utils/ltp/models/cws.models"
cfg.ltp_seg_lexicon_path = "../utils/ltp/user_lexicon.txt"
cfg.ltp_pos_model_path = "../utils/ltp/models/pos.models"
cfg.ltp_rec_model_path = "../utils/ltp/models/ner.models"
cfg.ltp_par_model_path = "../utils/ltp/models/parser.models"
dp_nlu = DpNLU(cfg)
from Chatbot.sessions.statement import Statement
from Chatbot.utils.preprocessor import Preprocessor
input_str = "我想听周杰伦的稻香"
pp = Preprocessor(cfg)
stmt = pp.process(Statement(text=input_str))
dp_nlu.process(stmt)
def dependency_parse(self, seg, pos):
# 调用pyltp进行依存句法分析
arcs = self.ltp_util.Parser(seg, pos)
parse_result = []
for item in list(arcs):
parse_result.append((item.head - 1, item.relation))
return parse_result
def emotion_analysis(self, text, contexts):
# 返回情感分析结果
return None
if __name__ == "__main__":
from Chatbot.sessions.statement import Statement
from Chatbot.configs import Configs
cfg = Configs()
cfg.ltp_seg_model_path = "./ltp/models/cws.model"
cfg.ltp_seg_lexicon_path = "./ltp/user_lexicon.txt"
cfg.ltp_pos_model_path = "./ltp/models/pos.model"
cfg.ltp_rec_model_path = "./ltp/models/ner.model"
cfg.ltp_par_model_path = "./ltp/models/parser.model"
p = Preprocessor(configs=cfg)
stmt = p.process(Statement(u"这是一个测试句子。"))
elif "SEARCHBYTYPE" in res: # 按歌曲类型搜索
type = res.strip().split(":")[-1]
return "SearchByType", {"type": type}
elif "SEARCHBYSCENE" in res: # 按场景搜索
scene = res.strip().split(":")[-1]
return "SearchByScene", {"scene": scene}
else:
return None, None
def entity_recognizer(self, text):
# 通过字典匹配、知识图谱等等技术手段判断实体类型
pass
return "SINGER"
if __name__ == "__main__":
from Chatbot.configs import Configs
from Chatbot.sessions.statement import Statement
cfg = Configs()
cfg.aiml_nlu_path = "../aiml/nlu-startup.xml"
nlu = AimlNLU(cfg)
nlu.process(Statement(text="搜 haoting 的歌"))
elif "SEARCHBYTYPE" in res: # 按歌曲类型搜索
type = res.strip().split(":")[-1]
return "SearchByType", {"type": type}
elif "SEARCHBYSCENE" in res: # 按场景搜索
scene = res.strip().split(":")[-1]
return "SearchByScene", {"scene": scene}
else:
return None, None
def entity_recognizer(self, text):
# 通过字典匹配、知识图谱等等技术手段判断实体类型
pass
return "SINGER"
if __name__ == "__main__":
from Chatbot.configs import Configs
from Chatbot.sessions.statement import Statement
cfg = Configs()
cfg.aiml_nlu_path = "../aiml/nlu-startup.xml"
nlu = AimlNLU(cfg)
nlu.process(Statement(text="搜好听的歌"))
def process(self, status, target, policy):
return Statement(text="默认回复。", from_user=False)