NLP（九） 文本相似度问题

原文链接：http://www.one2know.cn/nlp9/

• 多个维度判别文本之间相似度

1. 情感维度 Sentiment/Emotion
2. 感官维度 Sense
3. 特定词的出现

• 词频 TF
  逆文本频率 IDF
  构建N个M维向量，N是文档总数，M是所有文档的去重词汇量
• 余弦相似度：
  
  A,B分别是两个词的向量

import nltk
import math
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

class TextSimilarityExample:
    def __init__(self):
        self.statements = [ # 例句
            'ruled india',
            'Chalukyas ruled Badami',
            'So many kingdoms ruled India',
            'Lalbagh is a botanical garden in India',
        ]
    def TF(self,sentence):
        words = nltk.word_tokenize(sentence.lower()) # 分词,都化成小写
        freq = nltk.FreqDist(words) # 计算词频分布，词和词频组成的字典
        dictionary = {}
        for key in freq.keys():
            norm = freq[key] / float(len(words)) # 把词频正则化
            dictionary[key] = norm
        return dictionary # 返回 词:词频
    def IDF(self):
        def idf(TotalNumberOfDocuments,NumberOfDocumentsWithThisWord):
            return 1.0 + math.log(TotalNumberOfDocuments/NumberOfDocumentsWithThisWord)
        # idf = 1 + log(总文件数/含该词的文件数)
        numDocuments = len(self.statements) # 总文档数
        uniqueWords = {} # 不重复的 字典
        idfValues = {} # 词:IDF 字典
        for sentence in self.statements: # 得到每个句子的 词:含该词文章数量 字典
            for word in nltk.word_tokenize(sentence.lower()):
                if word not in uniqueWords:
                    uniqueWords[word] = 1
                else:
                    uniqueWords[word] += 1
        for word in uniqueWords: # 词:含该词文章数量 字典 => 词:IDF 字典
            idfValues[word] = idf(numDocuments,uniqueWords[word])
        return idfValues
    def TF_IDF(self,query): # 返回每句话的向量
        words = nltk.word_tokenize(query.lower())
        idf = self.IDF() # IDF 由所有文档求出
        vectors = {}
        for sentence in self.statements: # 遍历所有句子
            tf = self.TF(sentence) # TF 由单个句子得出
            for word in words:
                tfv = tf[word] if word in tf else 0.0
                idfv = idf[word] if word in idf else 0.0
                mul = tfv * idfv
                if word not in vectors:
                    vectors[word] = []
                vectors[word].append(mul) # 字典里添加元素7
        return vectors
    def displayVectors(self,vectors): # 显示向量内容
        print(self.statements)
        for word in vectors:
            print("{} -> {}".format(word,vectors[word]))
    def cosineSimilarity(self):
        vec = TfidfVectorizer() # 创建新的向量对象
        matrix = vec.fit_transform(self.statements) # 计算所有文本的TF-IDF值矩阵
        for j in range(1,5):
            i = j - 1
            print("	similarity of document {} with others".format(j))
            similarity = cosine_similarity(matrix[i:j],matrix) # scikit库的余弦相似度函数
            print(similarity)
    def demo(self):
        inputQuery = self.statements[0] # 第一个句子作为输入查询
        vectors = self.TF_IDF(inputQuery) # 建立第一句的向量
        self.displayVectors(vectors) # 屏幕上显示所有句子的TF×IDF向量
        self.cosineSimilarity() # 计算输入句子与所有句子的余弦相似度
if __name__ == "__main__":
    similarity = TextSimilarityExample()
    similarity.demo()

输出：

['ruled india', 'Chalukyas ruled Badami', 'So many kingdoms ruled India', 'Lalbagh is a botanical garden in India']
ruled -> [0.6438410362258904, 0.42922735748392693, 0.2575364144903562, 0.0]
india -> [0.6438410362258904, 0.0, 0.2575364144903562, 0.18395458177882582]
	similarity of document 1 with others
[[1.         0.29088811 0.46216171 0.19409143]]
	similarity of document 2 with others
[[0.29088811 1.         0.13443735 0.        ]]
	similarity of document 3 with others
[[0.46216171 0.13443735 1.         0.08970163]]
	similarity of document 4 with others
[[0.19409143 0.         0.08970163 1.        ]]