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。
demo
https://www.borntowin.cn/product/word_emb_sim
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
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。
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。
- Maven官方库
<dependency>
<groupId>io.github.shibing624</groupId>
<artifactId>similarity</artifactId>
<version>1.1.3</version>
</dependency>
import
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
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 = "**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。
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