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。
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()
您可能感兴趣的文章:python多进程提取处理大量文本的关键词方法python实现关键词提取的示例讲解python提取内容关键词的方法python TF-IDF算法实现文本关键词提取
