用python做一个可以回答任意问题的机器人
时间: 2024-02-01 19:04:31 浏览: 25
要做一个可以回答任意问题的机器人,需要更加复杂的自然语言处理技术和算法。以下是一个简单的例子,可以使用 Python 的 NLTK 库来实现:
```python
import nltk
import numpy as np
import random
import string
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# 预处理数据
nltk.download('punkt')
nltk.download('wordnet')
f = open('chatbot.txt', 'r', errors='ignore')
raw = f.read()
raw = raw.lower() # 转换为小写字母
sent_tokens = nltk.sent_tokenize(raw) # 将文本分成句子
word_tokens = nltk.word_tokenize(raw) # 将文本分成单词
lemmer = nltk.stem.WordNetLemmatizer()
def LemTokens(tokens):
return [lemmer.lemmatize(token) for token in tokens]
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
def LemNormalize(text):
return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))
# 定义机器人的问候语
GREETING_INPUTS = ["hello", "hi", "greetings", "sup", "what's up", "hey"]
GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]
def greeting(sentence):
for word in sentence.split():
if word.lower() in GREETING_INPUTS:
return random.choice(GREETING_RESPONSES)
# 生成回答
def response(user_response):
robo_response = ''
sent_tokens.append(user_response)
# 创建TF-IDF向量
TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')
tfidf = TfidfVec.fit_transform(sent_tokens)
# 计算用户输入和所有句子的相似度
vals = cosine_similarity(tfidf[-1], tfidf)
idx = vals.argsort()[0][-2]
# 获取相似度最高的句子
flat = vals.flatten()
flat.sort()
req_tfidf = flat[-2]
if req_tfidf == 0:
robo_response = robo_response + "I am sorry! I don't understand you"
return robo_response
else:
robo_response = robo_response + sent_tokens[idx]
return robo_response
# 与用户进行交互
flag = True
print("ROBO: My name is Robo. I will answer your queries about Chatbots. If you want to exit, type Bye!")
while flag == True:
user_response = input()
user_response = user_response.lower()
if user_response != 'bye':
if user_response == 'thanks' or user_response == 'thank you':
flag = False
print("ROBO: You are welcome..")
else:
if greeting(user_response) != None:
print("ROBO: " + greeting(user_response))
else:
print("ROBO: ", end="")
print(response(user_response))
sent_tokens.remove(user_response)
else:
flag = False
print("ROBO: Bye! take care..")
```
这个例子中,我们使用了 TF-IDF 算法来计算用户输入和所有句子的相似度,并返回相似度最高的句子作为回答。这个例子是比较简单的,如果您想让机器人更加智能和自然,您需要使用更高级的自然语言处理技术并进行更复杂的编程。
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