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.DS_Store 0 → 100644
File added
.ipynb_checkpoints/Untitled-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/Users/zeyusu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
]
}
],
"source": [
"import pandas as pd\n",
"import fool\n",
"import time\n",
"import re\n",
"import random\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stopwords = {}\n",
"with open(r'stopword.txt', 'r', encoding='utf-8') as fr:\n",
" for word in fr:\n",
" stopwords[word.strip()] = 0"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class CLF_MODEL:\n",
" # 类目标：该类将所有模型训练、预测、数据预处理、意图识别的函数包括其中\n",
"\n",
" # 初始化模块\n",
" def __init__(self):\n",
" self.model = \"\" # 成员变量，用于存储模型\n",
" self.vectorizer = \"\" # 成员变量，用于存储tfidf统计值\n",
"\n",
" # 训练模块\n",
" def train(self):\n",
" # 函数目标：读取训练数据，训练意图分类模型，并将训练好的分类模型赋值给成员变量self.model\n",
" # input：无\n",
" # output：无\n",
"\n",
" # 从excel文件读取训练样本\n",
" d_train = pd.read_excel(\"data_train.xlsx\")\n",
" # 对训练数据进行预处理\n",
" d_train.sentence_train = d_train.sentence_train.apply(self.fun_clean)\n",
" print(\"训练样本 = %d\" % len(d_train))\n",
"\n",
" \"\"\"\n",
" TODO：利用sklearn中的函数进行训练，将句子转化为特征features\n",
" \"\"\"\n",
" self.vectorizer = TfidfVectorizer()\n",
" X = self.vectorizer.fit_transform(d_train.sentence_train)\n",
" self.model = LogisticRegression(penalty='l1', solver='liblinear')\n",
"\n",
" self.model.fit(X, d_train.label)\n",
"\n",
" # 预测模块（使用模型预测）\n",
" def predict_model(self, sentence):\n",
" # 函数目标：使用意图分类模型预测意图\n",
" # input：sentence（用户输入）\n",
" # output：clf_result（意图类别），score（意图分数）\n",
"\n",
" # --------------\n",
" # 对样本中没有的特殊情况做特别判断\n",
" if sentence in [\"好的\", \"需要\", \"是的\", \"要的\", \"好\", \"要\", \"是\"]:\n",
" return 1, 0.8\n",
" # --------------\n",
"\n",
" \"\"\"\n",
" TODO：利用已训练好的意图分类模型进行意图识别\n",
" \"\"\"\n",
" sentence = self.fun_clean(sentence)\n",
" x = self.vectorizer.transform([sentence])\n",
" clf_result = self.model.predict(x)[0]\n",
" score = self.model.predict_proba(x)[0][clf_result]\n",
"\n",
"\n",
" return clf_result, score\n",
"\n",
" # 预测模块（使用规则）\n",
" def predict_rule(self, sentence):\n",
" # 函数目标：如果模型训练出现异常，可以使用规则进行预测，同时也可以让学员融合\"模型\"及\"规则\"的预测方式\n",
" # input：sentence（用户输入）\n",
" # output：clf_result（意图类别），score（意图分数）\n",
"\n",
" sentence = sentence.replace(' ', '')\n",
" if re.findall(r'不需要|不要|停止|终止|退出|不买|不定|不订', sentence):\n",
" return 2, 0.8\n",
" elif re.findall(r'订|定|预定|买|购', sentence) or sentence in [\"好的\",\"需要\",\"是的\",\"要的\",\"好\",\"要\",\"是\"]:\n",
" return 1, 0.8\n",
" else:\n",
" return 0, 0.8\n",
"\n",
" # 预处理函数\n",
" def fun_clean(self, sentence):\n",
" # 函数目标：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等\n",
" # input：sentence（用户输入语句）\n",
" # output：sentence（预处理结果）\n",
"\n",
" \"\"\"\n",
" TODO：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等\n",
" \"\"\"\n",
" words, ners = fool.analysis(sentence)\n",
"\n",
" res = []\n",
" ner_idx = 0\n",
" cur_idx = 0\n",
" for word in words[0]:\n",
" if ner_idx < len(ners[0]) and cur_idx == ners[0][ner_idx][0]:\n",
" if ners[0][ner_idx][0] == 'time':\n",
" res.append('DATE')\n",
" elif ners[0][ner_idx][0] == 'location':\n",
" res.append('CITY')\n",
" elif word[0] not in stopwords:\n",
" res.append(word[0])\n",
" ner_idx += 1\n",
" elif word[0] not in stopwords:\n",
" res.append(word[0])\n",
" cur_idx += len(word)\n",
" \n",
" return ' '.join(res)\n",
"\n",
" # 分类主函数\n",
" def fun_clf(self, sentence):\n",
" # 函数目标：意图识别主函数\n",
" # input：sentence（ 用户输入语句）\n",
" # output：clf_result（意图类别），score（意图分数）\n",
"\n",
" # 对用户输入进行预处理\n",
" sentence = self.fun_clean(sentence)\n",
" # 得到意图分类结果（0为“查询”类别，1为“订票”类别，2为“终止服务”类别）\n",
" clf_result, score = self.predict_model(sentence) # 使用训练的模型进行意图预测\n",
" # clf_result, score = self.predict_rule(sentence) # 使用规则进行意图预测（可与用模型进行意图识别的方法二选一）\n",
" return clf_result, score"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sentence = '帮 我 查 一下 DATE 去 CITY 的 票'"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"训练样本 = 99\n"
]
}
],
"source": [
"clf_obj = CLF_MODEL()\n",
"# 完成意图识别模型的训练\n",
"clf_obj.train()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf_result, score = clf_obj.predict_model(sentence)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fun_replace_num(sentence):\n",
" # 函数目标：替换时间中的数字（目的是便于实体识别包fool对实体的识别）\n",
" # input：sentence\n",
" # output：sentence\n",
"\n",
" # 定义要替换的数字\n",
" time_num = {\"一\":\"1\",\"二\":\"2\",\"三\":\"3\",\"四\":\"4\",\"五\":\"5\",\"六\":\"6\",\"七\":\"7\",\"八\":\"8\",\"九\":\"9\",\"十\":\"10\",\"十一\":\"11\",\"十二\":\"12\"}\n",
" for k, v in time_num.items():\n",
" sentence = sentence.replace(k, v)\n",
" return sentence"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def slot_fill(sentence, key=None):\n",
" # 函数目标：填槽函数（该函数从sentence中寻找需要的内容，完成填槽工作）\n",
" # input：sentence（用户输入）, key（指定槽位，只对该句话提取指定槽位的信息）\n",
" # output：slot（返回填槽的结果，以json格式返回，key为槽位名，value为值）\n",
"\n",
" slot = {}\n",
" # 进行实体识别\n",
" slot_lst = {\"time\":\"time\", \"date\":\"time\", \"from_city\":'location', \"to_city\":'location'}\n",
" words, ners = fool.analysis(sentence)\n",
" \"\"\"\n",
" TODO：从sentence中寻找需要的内容，完成填槽工作\n",
" \"\"\"\n",
" if not key:\n",
" keys = [\"time\", \"date\", \"from_city\", \"to_city\"]\n",
" else:\n",
" keys = [key]\n",
" for key in keys:\n",
" print(key)\n",
" if ners != [[]]:\n",
" for i in range(len(ners[0])):\n",
" if ners[0][i][2] == slot_lst[key]:\n",
" if key == \"time\" and not isTimeFormat(ners[0][i][3]):\n",
" continue\n",
" slot[key] = ners[0][i][3]\n",
" del ners[0][i]\n",
" break\n",
" return slot\n",
"\n",
"\n",
"def fun_wait(clf_obj):\n",
" # 函数目标：等待，获取用户输入问句\n",
" # input：CLF_MODEL类实例化对象\n",
" # output：clf_result（用户输入意图类别）, score（意图识别分数）, sentence（用户输入）\n",
"\n",
" # 等待用户输入\n",
" print(\"\\n\\n\\n\")\n",
" print(\"-------------------------------------------------------------\")\n",
" print(\"----*------*-----*-----*----*-----*-----*-----*-----*------\")\n",
" print(\"Starting ...\")\n",
" sentence = input(\"客服：请问需要什么服务？(时间请用12小时制表示）\\n\")\n",
" # 对用户输入进行意图识别\n",
" clf_result, score = clf_obj.fun_clf(sentence)\n",
" return clf_result, score, sentence\n",
"\n",
"\n",
"def fun_search(clf_result, sentence):\n",
" # 函数目标：为用户查询余票\n",
" # input：clf_result（意图分类结果）, sentence（用户输入问句）\n",
" # output：是否有票\n",
"\n",
" # 定义槽存储空间\n",
" name = {\"time\":\"出发时间\", \"date\":\"出发日期\", \"from_city\":\"出发城市\", \"to_city\":\"到达城市\"}\n",
" slot = {\"time\":\"\", \"date\":\"\", \"from_city\":\"\", \"to_city\":\"\"}\n",
" # 使用用户第一句话进行填槽\n",
" sentence = fun_replace_num(sentence)\n",
" slot_init = slot_fill(sentence)\n",
" for key in slot_init.keys():\n",
" slot[key] = slot_init[key]\n",
" # 对未填充对槽位，向用户提问，进行针对性填槽\n",
" while \"\" in slot.values():\n",
" for key in slot.keys():\n",
" if slot[key]==\"\":\n",
" sentence = input(\"客服：请问%s是？\\n\"%(name[key]))\n",
" sentence = fun_replace_num(sentence)\n",
" slot_cur = slot_fill(sentence, key)\n",
" for key in slot_cur.keys():\n",
" if slot[key]==\"\":\n",
" slot[key] = slot_cur[key]\n",
"\n",
" # 查询是否有票，并答复用户（本次查询是否有票使用随机数完成，实际情况可查询数据库返回）\n",
" if random.random()>0.5:\n",
" print(\"客服：%s%s从%s到%s的票充足\"%(slot[\"date\"], slot[\"time\"], slot[\"from_city\"], slot[\"to_city\"]))\n",
" # 返回1表示有票\n",
" return 1\n",
" else:\n",
" print(\"客服：%s%s从%s到%s无票\" % (slot[\"date\"], slot[\"time\"], slot[\"from_city\"], slot[\"to_city\"]))\n",
" print(\"End !!!\")\n",
" print(\"----*------*-----*-----*----*-----*-----*-----*-----*------\")\n",
" print(\"-------------------------------------------------------------\")\n",
" # 返回0表示无票\n",
" return 0\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<_sre.SRE_Match object; span=(0, 3), match='4pm'>\n"
]
}
],
"source": [
"time='4pm' \n",
"print(re.match('^\\d{1,2}([:.]?\\d{1,2})?([ ]?[a|p]m)?$',time))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[('4', 'm'), ('pm', 'nx')]]\n",
"[[]]\n"
]
}
],
"source": [
"s = '4pm'\n",
"words, ners = fool.analysis(s)\n",
"print(words)\n",
"print(ners)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Readme.txt 0 → 100644
project2_main.py python执行脚本（所有的代码位置）
data_train.xlsx 意图分类训练数据
stopword.txt 停用词
data_train.xlsx 0 → 100644
File added
project2_main.py 0 → 100644
# coding=utf-8
import pandas as pd
import fool
import re
import random
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
# -----------------------------------------------------
# 加载停用词词典
stopwords = {}
with open(r'stopword.txt', 'r', encoding='utf-8') as fr:
for word in fr:
stopwords[word.strip()] = 0
# -----------------------------------------------------
# 定义类
class CLF_MODEL:
# 类目标：该类将所有模型训练、预测、数据预处理、意图识别的函数包括其中
# 初始化模块
def __init__(self):
self.model = "" # 成员变量，用于存储模型
self.vectorizer = "" # 成员变量，用于存储tfidf统计值
# 训练模块
def train(self):
# 函数目标：读取训练数据，训练意图分类模型，并将训练好的分类模型赋值给成员变量self.model
# input：无
# output：无
# 从excel文件读取训练样本
d_train = pd.read_excel("data_train.xlsx")
# 对训练数据进行预处理
d_train.sentence_train = d_train.sentence_train.apply(self.fun_clean)
print("训练样本 = %d" % len(d_train))
"""
TODO：利用sklearn中的函数进行训练，将句子转化为特征features
"""
self.vectorizer = TfidfVectorizer()
X = self.vectorizer.fit_transform(d_train.sentence_train)
self.model = LogisticRegression(penalty='l1', solver='liblinear')
self.model.fit(X, d_train.label)
# 预测模块（使用模型预测）
def predict_model(self, sentence):
# 函数目标：使用意图分类模型预测意图
# input：sentence（用户输入）
# output：clf_result（意图类别），score（意图分数）
# --------------
# 对样本中没有的特殊情况做特别判断
if sentence in ["好的", "需要", "是的", "要的", "好", "要", "是"]:
return 1, 0.8
# --------------
"""
TODO：利用已训练好的意图分类模型进行意图识别
"""
x = self.vectorizer.transform([sentence])
clf_result = self.model.predict(x)[0]
score = self.model.predict_proba(x)[0][clf_result]
return clf_result, score
# 预测模块（使用规则）
def predict_rule(self, sentence):
# 函数目标：如果模型训练出现异常，可以使用规则进行预测，同时也可以让学员融合"模型"及"规则"的预测方式
# input：sentence（用户输入）
# output：clf_result（意图类别），score（意图分数）
sentence = sentence.replace(' ', '')
if re.findall(r'不需要|不要|停止|终止|退出|不买|不定|不订', sentence):
return 2, 0.8
elif re.findall(r'订|定|预定|买|购', sentence) or sentence in ["好的","需要","是的","要的","好","要","是"]:
return 1, 0.8
else:
return 0, 0.8
# 预处理函数
def fun_clean(self, sentence):
# 函数目标：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等
# input：sentence（用户输入语句）
# output：sentence（预处理结果）
"""
TODO：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等
"""
words, ners = fool.analysis(sentence)
res = []
ner_idx = 0
cur_idx = 0
for word in words[0]:
if ner_idx < len(ners[0]) and cur_idx == ners[0][ner_idx][0]:
if ners[0][ner_idx][2] == 'time':
res.append('DATE')
elif ners[0][ner_idx][2] == 'location':
res.append('CITY')
elif word[0] not in stopwords:
res.append(word[0])
ner_idx += 1
elif word[0] not in stopwords:
res.append(word[0])
cur_idx += len(word[0])
return ' '.join(res)
# 分类主函数
def fun_clf(self, sentence):
# 函数目标：意图识别主函数
# input：sentence（ 用户输入语句）
# output：clf_result（意图类别），score（意图分数）
# 对用户输入进行预处理
sentence = self.fun_clean(sentence)
# 得到意图分类结果（0为“查询”类别，1为“订票”类别，2为“终止服务”类别）
clf_result, score = self.predict_model(sentence) # 使用训练的模型进行意图预测
# clf_result, score = self.predict_rule(sentence) # 使用规则进行意图预测（可与用模型进行意图识别的方法二选一）
return clf_result, score
def fun_replace_num(sentence):
# 函数目标：替换时间中的数字（目的是便于实体识别包fool对实体的识别）
# input：sentence
# output：sentence
# 定义要替换的数字
time_num = {"一":"1","二":"2","三":"3","四":"4","五":"5","六":"6","七":"7","八":"8","九":"9","十":"10","十一":"11","十二":"12"}
for k, v in time_num.items():
sentence = sentence.replace(k, v)
return sentence
def slot_fill(sentence, key=None):
# 函数目标：填槽函数（该函数从sentence中寻找需要的内容，完成填槽工作）
# input：sentence（用户输入）, key（指定槽位，只对该句话提取指定槽位的信息）
# output：slot（返回填槽的结果，以json格式返回，key为槽位名，value为值）
slot = {}
# 进行实体识别
slot_lst = {"time":"time", "date":"time", "from_city":'location', "to_city":'location'}
words, ners = fool.analysis(sentence)
"""
TODO：从sentence中寻找需要的内容，完成填槽工作
"""
if not key:
keys = ["time", "date", "from_city", "to_city"]
else:
keys = [key]
for key in keys:
if ners != [[]]:
for i in range(len(ners[0])):
if ners[0][i][2] == slot_lst[key]:
if key == "time" and not re.match('^\d{1,2}([:.]?\d{1,2})?([ ]?[a|p]m)?$',ners[0][i][3]):
continue
slot[key] = ners[0][i][3]
del ners[0][i]
break
return slot
def fun_wait(clf_obj):
# 函数目标：等待，获取用户输入问句
# input：CLF_MODEL类实例化对象
# output：clf_result（用户输入意图类别）, score（意图识别分数）, sentence（用户输入）
# 等待用户输入
print("\n\n\n")
print("-------------------------------------------------------------")
print("----*------*-----*-----*----*-----*-----*-----*-----*------")
print("Starting ...")
sentence = input("客服：请问需要什么服务？(时间请用12小时制表示）\n")
# 对用户输入进行意图识别
clf_result, score = clf_obj.fun_clf(sentence)
return clf_result, score, sentence
def fun_search(clf_result, sentence):
# 函数目标：为用户查询余票
# input：clf_result（意图分类结果）, sentence（用户输入问句）
# output：是否有票
# 定义槽存储空间
name = {"time":"出发时间", "date":"出发日期", "from_city":"出发城市", "to_city":"到达城市"}
slot = {"time":"", "date":"", "from_city":"", "to_city":""}
# 使用用户第一句话进行填槽
sentence = fun_replace_num(sentence)
slot_init = slot_fill(sentence)
for key in slot_init.keys():
slot[key] = slot_init[key]
# 对未填充对槽位，向用户提问，进行针对性填槽
while "" in slot.values():
for key in slot.keys():
if slot[key]=="":
sentence = input("客服：请问%s是？\n"%(name[key]))
sentence = fun_replace_num(sentence)
slot_cur = slot_fill(sentence, key)
for key in slot_cur.keys():
if slot[key]=="":
slot[key] = slot_cur[key]
# 查询是否有票，并答复用户（本次查询是否有票使用随机数完成，实际情况可查询数据库返回）
if random.random()>0.5:
print("客服：%s%s从%s到%s的票充足"%(slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
# 返回1表示有票
return 1
else:
print("客服：%s%s从%s到%s无票" % (slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
print("End !!!")
print("----*------*-----*-----*----*-----*-----*-----*-----*------")
print("-------------------------------------------------------------")
# 返回0表示无票
return 0
def fun_book():
# 函数目标：执行下单订票动作
# input：无
# output：无
print("客服：已为您完成订票。\n\n\n")
print("End !!!")
print("----*------*-----*-----*----*-----*-----*-----*-----*------")
print("-------------------------------------------------------------")
if __name__=="__main__":
# 实例化对象
clf_obj = CLF_MODEL()
# 完成意图识别模型的训练
clf_obj.train()
# 用户定义阈值（当分类器分类的分数大于阈值才采纳本次意图分类结果，目的是排除分数过低的意图分类结果）
threshold = 0.55
# 循环提供服务
while 1:
clf_result, score, sentence = fun_wait(clf_obj)
# -------------------------------------------------------------------------------
# 状态转移条件（等待-->等待）：用户输入未达到“查询”、“订票”类别的阈值 OR 意图被分类为“终止服务”
# -------------------------------------------------------------------------------
if score<threshold or clf_result==2:
continue
# -------------------------------------------------------------------------------
# 状态转移条件（等待-->查询）：用户输入分类为“查询” OR “订票”
# -------------------------------------------------------------------------------
else:
# 收集订票细节信息
search_result = fun_search(clf_result, sentence)
# 查询无票
# -------------------------------------------------------------------------------
# 状态转移条件（查询-->等待）：FUN_SEARCH执行完后用户输入意图为“终止服务” OR FUN_SEARCH返回无票
# -------------------------------------------------------------------------------
if search_result==0:
continue
# 查询有票
else:
# 等待用户输入
sentence = input("客服：需要为您订票吗？\n")
# 对用户输入进行意图识别
clf_result, score = clf_obj.fun_clf(sentence)
# -------------------------------------------------------------------------------
# 状态转移条件（查询-->订票）：FUN_SEARCH返回有票 AND 用户输入意图为“订票”
# -------------------------------------------------------------------------------
if clf_result == 1:
fun_book()
continue
stopword.txt 0 → 100644
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