python实现textrank关键词提取

用python写了一个简单版本的textrank，实现提取关键词的功能。importnumpyasnpimportjiebaimportjieba.po

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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