文本相似度计算_java hanlp 文本相似度

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;

/**
 * @author zhencong_star
 * @date 2020/8/26 16:11
 */
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tag
        content = Jsoup.clean(content, Whitelist.none());
        content = StringUtils.lowerCase(content);
        String[] strings = {" ", "\n", "\r", "\t", "\\r", "\\n", "\\t", " "};
        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;
    }

    /**
     * 计算文本相似度
     * @param s2
     * @return
     */
    public double getSemblance(MySimHash s2 ){
        double i = (double) this.hammingDistance(s2);
        return 1 - i/this.hashbits ;
    }

    public static void main(String[] args) {

        String s1 = "  最后形成去掉噪音词的只要相似的字符串只有个别的位数是有差别变化。";
        String s2 = "  最1后1形1成1去1掉1噪1音1词1的1只1要1相1似1的1字1符1串1只1有1个1别1的1位1数1是1有1差1别1变1化1。";
        long l3 = System.currentTimeMillis();
        MySimHash hash1 = new MySimHash(s1, 64);
        MySimHash hash2 = new MySimHash(s2, 64);
        System.out.println("======================================");
        System.out.println(  hash1.hammingDistance(hash2) );
        System.out.println(  hash1.getSemblance(hash2) );
        long l4 = System.currentTimeMillis();
        System.out.println(l4-l3);
        System.out.println("======================================");
    }
}