narf关键点提取代码

下面是使用Python中的nltk库实现关键点提取的示例代码：

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import PorterStemmer

def extract_keypoints(text):
    # 将文本转换为小写
    text = text.lower()

    # 将文本分成句子
    sentences = sent_tokenize(text)

    # 创建 PorterStemmer 对象
    ps = PorterStemmer()

    # 获取停用词
    stop_words = set(stopwords.words('english'))

    keypoints = []

    # 遍历每个句子
    for sentence in sentences:
        # 分词
        words = word_tokenize(sentence)

        # 去除停用词、标点符号和数字
        filtered_words = [word for word in words if word.isalpha() and word not in stop_words]

        # 提取关键点
        for word in filtered_words:
            keypoint = ps.stem(word)
            if keypoint not in keypoints:
                keypoints.append(keypoint)

    return keypoints

使用示例：

text = "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data."

keypoints = extract_keypoints(text)

print(keypoints)

输出结果：

['natur', 'languag', 'process', 'nlp', 'subfield', 'linguist', 'comput', 'scienc', 'artifici', 'intellig', 'concern', 'interact', 'human', 'particular', 'program', 'analyz', 'larg', 'amount', 'data']