python实现textrank关键词提取

• Z时代
• 2024-01-10
• 分类：综合

用python写了一个简单版本的textrank，实现提取关键词的功能。

import numpy as np 
import jieba 
import jieba.posseg as pseg 
class TextRank(object): 
  def __init__(self, sentence, window, alpha, iternum): 
    self.sentence = sentence 
    self.window = window 
    self.alpha = alpha 
    self.edge_dict = {} #记录节点的边连接字典 
    self.iternum = iternum#迭代次数 
  #对句子进行分词 
  def cutSentence(self): 
    jieba.load_userdict('user_dict.txt') 
    tag_filter = ['a','d','n','v'] 
    seg_result = pseg.cut(self.sentence) 
    self.word_list = [s.word for s in seg_result if s.flag in tag_filter] 
    print(self.word_list) 
  #根据窗口，构建每个节点的相邻节点,返回边的集合 
  def createNodes(self): 
    tmp_list = [] 
    word_list_len = len(self.word_list) 
    for index, word in enumerate(self.word_list): 
      if word not in self.edge_dict.keys(): 
        tmp_list.append(word) 
        tmp_set = set() 
        left = index - self.window + 1#窗口左边界 
        right = index + self.window#窗口右边界 
        if left < 0: left = 0 
        if right >= word_list_len: right = word_list_len 
        for i in range(left, right): 
          if i == index: 
            continue 
          tmp_set.add(self.word_list[i]) 
        self.edge_dict[word] = tmp_set 
  #根据边的相连关系，构建矩阵 
  def createMatrix(self): 
    self.matrix = np.zeros([len(set(self.word_list)), len(set(self.word_list))]) 
    self.word_index = {}#记录词的index 
    self.index_dict = {}#记录节点index对应的词 
    for i, v in enumerate(set(self.word_list)): 
      self.word_index[v] = i 
      self.index_dict[i] = v 
    for key in self.edge_dict.keys(): 
      for w in self.edge_dict[key]: 
        self.matrix[self.word_index[key]][self.word_index[w]] = 1 
        self.matrix[self.word_index[w]][self.word_index[key]] = 1 
    #归一化 
    for j in range(self.matrix.shape[1]): 
      sum = 0 
      for i in range(self.matrix.shape[0]): 
        sum += self.matrix[i][j] 
      for i in range(self.matrix.shape[0]): 
        self.matrix[i][j] /= sum 
  #根据textrank公式计算权重 
  def calPR(self): 
    self.PR = np.ones([len(set(self.word_list)), 1]) 
    for i in range(self.iternum): 
      self.PR = (1 - self.alpha) + self.alpha * np.dot(self.matrix, self.PR) 
  #输出词和相应的权重 
  def printResult(self): 
    word_pr = {} 
    for i in range(len(self.PR)): 
      word_pr[self.index_dict[i]] = self.PR[i][0] 
    res = sorted(word_pr.items(), key = lambda x : x[1], reverse=True) 
    print(res) 
if __name__ == '__main__': 
  s = '程序员(英文Programmer)是从事程序开发、维护的专业人员。一般将程序员分为程序设计人员和程序编码人员，但两者的界限并不非常清楚，特别是在中国。软件从业人员分为初级程序员、高级程序员、系统分析员和项目经理四大类。' 
  tr = TextRank(s, 3, 0.85, 700) 
  tr.cutSentence() 
  tr.createNodes() 
  tr.createMatrix() 
  tr.calPR() 
  tr.printResult() 

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