【java 走进NLP】simhash 算法计算两篇文章相似度

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python 计算两篇文章的相似度算法simhash见：
https://blog.csdn.net/u013421629/article/details/85052915

对长文本 是比较合适的（超过500字以上）
下面贴上java 版本实现：

pom.xml 加入依赖

<dependency><groupId>org.jsoup</groupId><artifactId>jsoup</artifactId><version>1.8.1</version>
</dependency>

创建文件
MySimHash.java

/*计算两篇文章相似度*/
import com.hankcs.hanlp.seg.common.Term;
import com.hankcs.hanlp.tokenizer.StandardTokenizer;
import org.apache.commons.lang3.StringUtils;
import org.jsoup.Jsoup;
import org.jsoup.safety.Whitelist;
import java.math.BigInteger;
import java.util.HashMap;
import java.util.List;
import java.util.Map;public class MySimHash {private String tokens; //字符串private BigInteger strSimHash;//字符产的hash值private int hashbits = 64; // 分词后的hash数;public MySimHash(String tokens) {this.tokens = tokens;this.strSimHash = this.simHash();}private MySimHash(String tokens, int hashbits) {this.tokens = tokens;this.hashbits = hashbits;this.strSimHash = this.simHash();}/*** 清除html标签* @param content* @return*/private String cleanResume(String content) {// 若输入为HTML,下面会过滤掉所有的HTML的tagcontent = Jsoup.clean(content, Whitelist.none());content = StringUtils.lowerCase(content);String[] strings = {" ", "\n", "\r", "\t", "\\r", "\\n", "\\t", "&nbsp;"};for (String s : strings) {content = content.replaceAll(s, "");}return content;}/*** 这个是对整个字符串进行hash计算* @return*/private BigInteger simHash() {tokens = cleanResume(tokens); // cleanResume 删除一些特殊字符int[] v = new int[this.hashbits];List<Term> termList = StandardTokenizer.segment(this.tokens); // 对字符串进行分词//对分词的一些特殊处理 : 比如: 根据词性添加权重 , 过滤掉标点符号 , 过滤超频词汇等;Map<String, Integer> weightOfNature = new HashMap<String, Integer>(); // 词性的权重weightOfNature.put("n", 2); //给名词的权重是2;Map<String, String> stopNatures = new HashMap<String, String>();//停用的词性 如一些标点符号之类的;stopNatures.put("w", ""); //int overCount = 5; //设定超频词汇的界限 ;Map<String, Integer> wordCount = new HashMap<String, Integer>();for (Term term : termList) {String word = term.word; //分词字符串String nature = term.nature.toString(); // 分词属性;//  过滤超频词if (wordCount.containsKey(word)) {int count = wordCount.get(word);if (count > overCount) {continue;}wordCount.put(word, count + 1);} else {wordCount.put(word, 1);}// 过滤停用词性if (stopNatures.containsKey(nature)) {continue;}// 2、将每一个分词hash为一组固定长度的数列.比如 64bit 的一个整数.BigInteger t = this.hash(word);for (int i = 0; i < this.hashbits; i++) {BigInteger bitmask = new BigInteger("1").shiftLeft(i);// 3、建立一个长度为64的整数数组(假设要生成64位的数字指纹,也可以是其它数字),// 对每一个分词hash后的数列进行判断,如果是1000...1,那么数组的第一位和末尾一位加1,// 中间的62位减一,也就是说,逢1加1,逢0减1.一直到把所有的分词hash数列全部判断完毕.int weight = 1;  //添加权重if (weightOfNature.containsKey(nature)) {weight = weightOfNature.get(nature);}if (t.and(bitmask).signum() != 0) {// 这里是计算整个文档的所有特征的向量和v[i] += weight;} else {v[i] -= weight;}}}BigInteger fingerprint = new BigInteger("0");for (int i = 0; i < this.hashbits; i++) {if (v[i] >= 0) {fingerprint = fingerprint.add(new BigInteger("1").shiftLeft(i));}}return fingerprint;}/*** 对单个的分词进行hash计算;* @param source* @return*/private BigInteger hash(String source) {if (source == null || source.length() == 0) {return new BigInteger("0");} else {/*** 当sourece 的长度过短，会导致hash算法失效，因此需要对过短的词补偿*/while (source.length() < 3) {source = source + source.charAt(0);}char[] sourceArray = source.toCharArray();BigInteger x = BigInteger.valueOf(((long) sourceArray[0]) << 7);BigInteger m = new BigInteger("1000003");BigInteger mask = new BigInteger("2").pow(this.hashbits).subtract(new BigInteger("1"));for (char item : sourceArray) {BigInteger temp = BigInteger.valueOf((long) item);x = x.multiply(m).xor(temp).and(mask);}x = x.xor(new BigInteger(String.valueOf(source.length())));if (x.equals(new BigInteger("-1"))) {x = new BigInteger("-2");}return x;}}/*** 计算海明距离,海明距离越小说明越相似;* @param other* @return*/private int hammingDistance(MySimHash other) {BigInteger m = new BigInteger("1").shiftLeft(this.hashbits).subtract(new BigInteger("1"));BigInteger x = this.strSimHash.xor(other.strSimHash).and(m);int tot = 0;while (x.signum() != 0) {tot += 1;x = x.and(x.subtract(new BigInteger("1")));}return tot;}public double getSemblance(MySimHash s2 ){double i = (double) this.hammingDistance(s2);return 1 - i/this.hashbits ;}public static void main(String[] args) {String s1="simhash算法的主要思想是降维，将高维的特征向量映射成一个低维的特征向量，通过两个向量的Hamming Distance来确定文章是否重复或者高度近似。";String s2="simhash算法的主要思想是降维，将高维的特征向量映射成一个低维的特征向量，通过两个向量的Hamming Distance来确定文章是否重复或者高度近似。";String s3="SimHash算法是Google公司进行海量网页去重的高效算法，它通过将原始的文本映射为64位的二进制数字串，然后通过比较二进制数字串的差异进而来表示原始文本内容的差异。";long l3 = System.currentTimeMillis();MySimHash hash1 = new MySimHash(s1, 64);MySimHash hash2 = new MySimHash(s2, 64);MySimHash hash3 = new MySimHash(s3, 64);System.out.println("======================================");System.out.println(  hash1.hammingDistance(hash2) );System.out.println(  hash2.hammingDistance(hash3) );System.out.println(  hash1.getSemblance(hash3) );System.out.println(  hash2.getSemblance(hash3) );long l4 = System.currentTimeMillis();System.out.println("总共耗时:"+(l4-l3)+"毫秒");System.out.println("======================================");}
}

运行结果：

======================================
0
20
0.6875
0.6875
总共耗时:429毫秒
======================================Process finished with exit code 0

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