Awesome Open Source
Awesome Open Source

similarity

用于词语、短语、句子、词法分析、情感分析、语义分析等相关的相似度计算。

similarity是由一系列算法组成的Java版相似度计算工具包,目标是传播自然语言处理中相似度计算方法。similarity具备工具实用、性能高效、架构清晰、语料时新、可自定义的特点。


Feature

similarity提供下列功能:

  • 词语相似度计算

    • 词林编码法相似度
    • 汉语语义法相似度
    • 知网词语相似度
    • 字面编辑距离法
  • 短语相似度计算

    • 简单短语相似度
  • 句子相似度计算

    • 词性和词序结合法
    • 编辑距离算法
    • Gregor编辑距离法
    • 优化编辑距离法
  • 文本相似度计算

    • 余弦相似度
    • 编辑距离算法
    • 欧几里得距离
    • Jaccard相似性系数
    • Jaro距离
    • Jaro–Winkler距离
    • 曼哈顿距离
    • SimHash + 汉明距离
    • Sørensen–Dice系数
  • 词法分析

    • 中文分词
    • 分词词性标注
    • 词频统计
  • 知网义原

    • 义原树
  • 情感分析

    • 正面倾向程度
    • 负面倾向程度
    • 情感倾向性
  • 近似词

    • word2vec

在提供丰富功能的同时,similarity内部模块坚持低耦合、模型坚持惰性加载、词典坚持明文发布,使用方便,帮助用户训练自己的语料。


jar包

离线jar包

下载其中一个,置于项目Libraries下,这样加入到项目依赖即可。

jitpack库

<repositories>
  <repository>
    <id>jitpack.io</id>
    <url>https://jitpack.io</url>
  </repository>
</repositories>
<dependency>
  <groupId>com.github.shibing624</groupId>
  <artifactId>similarity</artifactId>
  <version>1.1</version>
</dependency>

Maven官方库(未上传,暂不可用)

由于maven官方库包上传需要审核校对,着实耗时,现提供离线版jar包,方便使用。后续可以切换到maven官方库调用。

<dependency>
  <groupId>io.github.shibing624</groupId>
  <artifactId>similarity</artifactId>
  <version>1.1.3</version>
</dependency>

导入包,使用示例:

import org.xm.Similarity;
import org.xm.tendency.word.HownetWordTendency;

public class demo {
    public static void main(String[] args) {
        double result = Similarity.cilinSimilarity("电动车", "自行车");
        System.out.println(result);

        String word = "混蛋";
        HownetWordTendency hownetWordTendency = new HownetWordTendency();
        result = hownetWordTendency.getTendency(word);
        System.out.println(word + "  词语情感趋势值:" + result);
    }
}

Usage

word similarity

demo code: src/test/java/org.xm/WordSimilarityDemo.java

package org.xm;

public class WordSimilarityDemo {

    public static void main(String[] args) {
        String word1 = "教师";
        String word2 = "教授";
        double cilinSimilarityResult = Similarity.cilinSimilarity(word1, word2);
        double pinyinSimilarityResult = Similarity.pinyinSimilarity(word1, word2);
        double conceptSimilarityResult = Similarity.conceptSimilarity(word1, word2);
        double charBasedSimilarityResult = Similarity.charBasedSimilarity(word1, word2);

        System.out.println(word1 + " vs " + word2 + " 词林相似度值:" + cilinSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 拼音相似度值:" + pinyinSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 概念相似度值:" + conceptSimilarityResult);
        System.out.println(word1 + " vs " + word2 + " 字面相似度值:" + charBasedSimilarityResult);
    }
}
  • result:

word_sim result

phrase similarity

demo code : src/test/java/org.xm/PhraseSimilarityDemo.java

public static void main(String[] args) {
    String phrase1 = "继续努力";
    String phrase2 = "持续发展";
    double result = Similarity.phraseSimilarity(phrase1, phrase2);

    System.out.println(phrase1 + " vs " + phrase2 + " 短语相似度值:" + result);
}
  • result:

phrase sim result

sentence similarity

demo code : src/test/java/org.xm/SentenceSimilarityDemo.java

public static void main(String[] args) {
    String sentence1 = "中国人爱吃鱼";
    String sentence2 = "湖北佬最喜吃鱼";

    double morphoSimilarityResult = Similarity.morphoSimilarity(sentence1, sentence2);
    double editDistanceResult = Similarity.editDistanceSimilarity(sentence1, sentence2);
    double standEditDistanceResult = Similarity.standardEditDistanceSimilarity(sentence1,sentence2);
    double gregeorEditDistanceResult = Similarity.gregorEditDistanceSimilarity(sentence1,sentence2);

    System.out.println(sentence1 + " vs " + sentence2 + " 词形词序句子相似度值:" + morphoSimilarityResult);
    System.out.println(sentence1 + " vs " + sentence2 + " 优化的编辑距离句子相似度值:" + editDistanceResult);
    System.out.println(sentence1 + " vs " + sentence2 + " 标准编辑距离句子相似度值:" + standEditDistanceResult);
    System.out.println(sentence1 + " vs " + sentence2 + " gregeor编辑距离句子相似度值:" + gregeorEditDistanceResult);
}
  • result:

sentence sim result

text similarity

demo code : src/test/java/org.xm/similarity/text/CosineSimilarityTest.java

@Test
public void getSimilarityScore() throws Exception {
    String text1 = "我爱购物";
    String text2 = "我爱读书";
    String text3 = "他是黑客";
    TextSimilarity similarity = new CosineSimilarity();
    double score1pk2 = similarity.getSimilarity(text1, text2);
    double score1pk3 = similarity.getSimilarity(text1, text3);
    double score2pk2 = similarity.getSimilarity(text2, text2);
    double score2pk3 = similarity.getSimilarity(text2, text3);
    double score3pk3 = similarity.getSimilarity(text3, text3);
    System.out.println(text1 + " 和 " + text2 + " 的相似度分值:" + score1pk2);
    System.out.println(text1 + " 和 " + text3 + " 的相似度分值:" + score1pk3);
    System.out.println(text2 + " 和 " + text2 + " 的相似度分值:" + score2pk2);
    System.out.println(text2 + " 和 " + text3 + " 的相似度分值:" + score2pk3);
    System.out.println(text3 + " 和 " + text3 + " 的相似度分值:" + score3pk3);

}
  • result:

cos text result

word frequency statistics

demo code : src/test/java/org/xm/tokenizer/WordFreqStatisticsTest.java

  • result:

word freq result

分词及词性标注内置调用HanLP,也可以使用NLPchina的ansj_seg分词工具。

sentiment analysis based on words

demo code : src/test/java/org/xm/tendency/word/HownetWordTendencyTest.java

@Test
public void getTendency() throws Exception {
    HownetWordTendency hownet = new HownetWordTendency();
    String word = "美好";
    double sim = hownet.getTendency(word);
    System.out.println(word + ":" + sim);
    System.out.println("混蛋:" + hownet.getTendency("混蛋"));
}
  • result:

tendency result

本例是基于义原树的词语粒度情感极性分析,关于文本情感分析有text-classifier,利用深度神经网络模型、SVM分类算法实现的效果更好。

homoionym(use word2vec)

demo code : src/test/java/org/xm/word2vec/Word2vecTest.java

@Test
public void testHomoionym() throws Exception {
    List<String> result = Word2vec.getHomoionym(RAW_CORPUS_SPLIT_MODEL, "武功", 10);
    System.out.println("武功 近似词:" + result);
}

@Test
public void testHomoionymName() throws Exception {
    String model = RAW_CORPUS_SPLIT_MODEL;
    List<String> result = Word2vec.getHomoionym(model, "乔帮主", 10);
    System.out.println("乔帮主 近似词:" + result);

    List<String> result2 = Word2vec.getHomoionym(model, "阿朱", 10);
    System.out.println("阿朱 近似词:" + result2);

    List<String> result3 = Word2vec.getHomoionym(model, "少林寺", 10);
    System.out.println("少林寺 近似词:" + result3);
}
  • train:

word2vec train

  • result:

word2vec result

训练词向量使用的是阿健实现的java版word2vec训练工具Word2VEC_java,训练语料是小说天龙八部,通过词向量实现得到近义词。 用户可以训练自定义语料,也可以用中文维基百科训练通用词向量。


Todo

文本相似性度量

  • [x] 关键词匹配(TF-IDF、BM25)
  • [x] 浅层语义匹配(WordEmbed隐语义模型,用word2vec或glove词向量直接累加构造的句向量)
  • [ ] 深度语义匹配模型(DSSM、CLSM、DeepMatch、MatchingFeatures、ARC-II、DeepMind,具体依次参考下面的Reference)

欢迎大家贡献代码及思路,完善本项目

Reference

  • [DSSM] Po-Sen Huang, et al., 2013, Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
  • [CLSM] Yelong Shen, et al, 2014, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
  • [DeepMatch] Zhengdong Lu & Hang Li, 2013, A Deep Architecture for Matching Short Texts
  • [MatchingFeatures] Zongcheng Ji, et al., 2014, An Information Retrieval Approach to Short Text Conversation
  • [ARC-II] Baotian Hu, et al., 2015, Convolutional Neural Network Architectures for Matching Natural Language Sentences
  • [DeepMind] Aliaksei Severyn, et al., 2015, Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

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