使用 TF-IDF 加权的空间向量模型实现句子相似度计算 字符匹配层次计算句子相似度 计算两个句子相似度的算法有很多种,但是对于从未了解过这方面算法的人来说,可能最容易想到的就是使用字符串匹配相关的算法,来检查两个句子所对应的字符串的字符相似程度.比如单纯的进行子串匹配,搜索 A 串中能与 B 串匹配的最大子串作为得分,亦或者用比较常见的最长公共子序列算法来衡量两个串的相似程度,使用编辑距离算法来衡量等. 上述基于字符匹配层次的算法一定程度上都可以计算出两个句子的相似度,不过他们只是单纯的从字符
Given two sentences words1, words2 (each represented as an array of strings), and a list of similar word pairs pairs, determine if two sentences are similar. For example, "great acting skills" and "fine drama talent" are similar, if th
本文作为笔者NLP入门系列文章第一篇,以后我们就要步入NLP时代. 本文将会介绍NLP中常见的词袋模型(Bag of Words)以及如何利用词袋模型来计算句子间的相似度(余弦相似度,cosine similarity). 首先,让我们来看一下,什么是词袋模型.我们以下面两个简单句子为例: sent1 = "I love sky, I love sea." sent2 = "I like running, I love reading." 通常,NL
Given two sentences words1, words2 (each represented as an array of strings), and a list of similar word pairs pairs, determine if two sentences are similar. For example, words1 = ["great", "acting", "skills"] and words2 = [&
Given two sentences words1, words2 (each represented as an array of strings), and a list of similar word pairs pairs, determine if two sentences are similar. For example, "great acting skills" and "fine drama talent" are similar, if th
import mathfrom math import isnanimport pandas as pd#结巴分词,切开之后,有分隔符def jieba_function(sent): import jieba sent1 = jieba.cut(sent) s = [] for each in sent1: s.append(each) return ' '.join(str(i) for i in s)def count_cos_similarity(vec_1, vec_2): if le
Given two sentences words1, words2 (each represented as an array of strings), and a list of similar word pairs pairs, determine if two sentences are similar. For example, words1 = ["great", "acting", "skills"] and words2 = [&
Given two sentences words1, words2 (each represented as an array of strings), and a list of similar word pairs pairs, determine if two sentences are similar. For example, words1 = ["great", "acting", "skills"] and words2 = [&
