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。

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

• xmnlp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。

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demo

https://www.borntowin.cn/product/word_emb_sim

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Todo

文本相似性度量

• 关键词匹配（TF-IDF、BM25）
• 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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Usage

word similarity

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);
}
    

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

• result:

phrase similarity

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);
}

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

• result:

sentence similarity

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);
}

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

• result:

text similarity

@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);

}

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

• result:

word frequency statistics

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

• result:

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

sentiment analysis based on words

@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"));
}

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

• 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)

@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);
}
    

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

• train:

• 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。