锦中人工智能助手

# 示例：教务智能助手的意图识别模块

import re

def detect_intent(user_input):

user_input = user_input.lower()

if re.search(r'课程|课表', user_input):

return 'course_schedule'

elif re.search(r'成绩|分数', user_input):

return 'grade_query'

elif re.search(r'考试|时间', user_input):

return 'exam_schedule'

else:

return 'unknown'

def extract_slots(intent, user_input):

slots = {}

if intent == 'course_schedule':

match = re.search(r'(?:\d{4}年)?[0-9]{2}级', user_input)

if match:

slots['class_year'] = match.group()

elif intent == 'grade_query':

match = re.search(r'(?:\d{4}年)?[0-9]{2}级', user_input)

if match:

slots['class_year'] = match.group()

match = re.search(r'([0-9]{4}-[0-9]{2}-[0-9]{2})', user_input)

if match:

slots['date'] = match.group()

return slots

# 测试

user_input = "我想查一下2023级学生的课程表"

intent = detect_intent(user_input)

slots = extract_slots(intent, user_input)

print("检测到意图:", intent)

print("提取的槽位:", slots)

# 示例：使用Scikit-learn训练意图分类器

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import make_pipeline

from sklearn.model_selection import train_test_split

# 假设我们有以下训练数据

texts = [

"我想查一下课程表",

"帮我查一下成绩",

"考试时间是什么时候",

"我想选课",

"帮我安排考试",

"我想知道我的学分",

]

labels = ['course_schedule', 'grade_query', 'exam_schedule', 'course_selection', 'exam_schedule', 'credit_info']

# 构建模型

model = make_pipeline(TfidfVectorizer(), MultinomialNB())

model.fit(texts, labels)

# 预测新输入

new_input = "考试时间是什么时候？"

predicted_intent = model.predict([new_input])

print("预测的意图:", predicted_intent[0])