基于ansj_seg和nlp-lang的简单nlp工具类
何正德
1、首先在pom中引入ansj_seg和nlp-lang的依赖包，
ansj_seg包的作用：
这是一个基于n-Gram+CRF+HMM的中文分词的java实现；
分词速度达到每秒钟大约200万字左右（mac air下测试），准确率能达到96%以上;
目前实现了.中文分词. 中文姓名识别 . 用户自定义词典,关键字提取，自动摘要，关键字标记等功能;
可以应用到自然语言处理等方面,适用于对分词效果要求高的各种项目;
nlp-lang包的作用(nlp常用工具和组件)：
工具：词语标准化、tire树结构、双数组tire树、文本断句、html标签清理、Viterbi算法增加；
组件：汉字转拼音、简繁体转换、bloomfilter、指纹去重、SimHash文章相似度计算、词贡献统计、基于内存的搜索提示、WordWeight词频统计,词idf统计,词类别相关度统计；
Maven：
<!-- nlp-lang -->
<dependency>
<groupId>org.nlpcn</groupId>
<artifactId>nlp-lang</artifactId>
<version>1.7.2</version>
</dependency>
<!-- ansj_seg -->
<dependency>
<groupId>org.ansj</groupId>
<artifactId>ansj_seg</artifactId>
<version>5.1.2</version>
</dependency>2、创建WordUtil类，如下：
package com.mengyao.nlp.util;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import org.ansj.app.keyword.KeyWordComputer;
import org.ansj.app.keyword.Keyword;
import org.ansj.app.summary.SummaryComputer;
import org.ansj.app.summary.pojo.Summary;
import org.ansj.domain.Result;
import org.ansj.domain.Term;
import org.ansj.splitWord.analysis.IndexAnalysis;
import org.ansj.splitWord.analysis.NlpAnalysis;
import org.ansj.splitWord.analysis.ToAnalysis;
import org.apache.commons.lang3.StringUtils;
import org.nlpcn.commons.lang.jianfan.JianFan;
import org.nlpcn.commons.lang.pinyin.Pinyin;
import org.nlpcn.commons.lang.util.WordAlert;
import org.nlpcn.commons.lang.util.WordWeight;
/**
*
* @author mengyao
*
*/
public class WordUtil {
public static void main(String[] args) {
System.out.println("2016/06/25".matches("^\\d{4}(\\-|\\/|\\.)\\d{1,2}\\1\\d{1,2}$"));
System.out.println("20160625".matches("^\\d{8}$"));
}
/**
* 文章摘要
* @param title
* @param content
* @return
*/
public static String getSummary(String title, String content) {
SummaryComputer summaryComputer = new SummaryComputer(title, content);
Summary summary = summaryComputer.toSummary();
return summary.getSummary();
}
/**
* 带标题的文章关键词提取
* @param title
* @param content
* @return
*/
public static List<Keyword> getKeyWord(String title, String content) {
List<Keyword> keyWords = new ArrayList<Keyword>();
KeyWordComputer<NlpAnalysis> kwc = new KeyWordComputer<NlpAnalysis>(20);
Collection<Keyword> result = kwc.computeArticleTfidf(title, content);
for (Keyword keyword : result) {
keyWords.add(keyword);
}
return keyWords;
}
/**
* 不带标题的文章关键词提取
* @param content
* @return
*/
public static List<Keyword> getKeyWord2(String content) {
List<Keyword> keyWords = new ArrayList<Keyword>();
KeyWordComputer<NlpAnalysis> kwc = new KeyWordComputer<NlpAnalysis>(20);
Collection<Keyword> result = kwc.computeArticleTfidf(content);
for (Keyword keyword : result) {
keyWords.add(keyword);
}
return keyWords;
}
/**
* 标准分词
* @param text
* @return
*/
public static List<Term> getToSeg(String text) {
List<Term> words = new ArrayList<Term>();
Result parse = ToAnalysis.parse(text);
for (Term term : parse) {
if (null!=term.getName()&&!term.getName().trim().isEmpty()) {
words.add(term);
}
}
return words;
}
/**
* NLP分词
* @param text
* @return
*/
public static List<Term> getNlpSeg(String text) {
List<Term> words = new ArrayList<Term>();
Result parse = NlpAnalysis.parse(text);
for (Term term : parse) {
if (null!=term.getName()&&!term.getName().trim().isEmpty()) {
words.add(term);
}
}
return words;
}
/**
* Index分词
* @param text
* @return
*/
public static List<Term> getIndexSeg(String text) {
List<Term> words = new ArrayList<Term>();
Result parse = IndexAnalysis.parse(text);
for (Term term : parse) {
if (null!=term.getName()&&!term.getName().trim().isEmpty()) {
words.add(term);
}
}
return words;
}
/**
* 简体转繁体
* @param word
* @return
*/
public static String jian2fan(String text) {
return JianFan.j2f(text);
}
/**
* 繁体转简体
* @param word
* @return
*/
public static String fan2jian(String text) {
return JianFan.f2j(text);
}
/**
* 拼音(不带音标)
* @param word
* @return
*/
public static String pinyin(String text) {
StringBuilder builder = new StringBuilder();
List<String> pinyins = Pinyin.pinyin(text);
for (String pinyin : pinyins) {
if (null != pinyin) {
builder.append(pinyin+" ");
}
}
return builder.toString();
}
/**
* 拼音(不带音标，首字母大写)
* @param word
* @return
*/
public static String pinyinUp(String text) {
StringBuilder builder = new StringBuilder();
List<String> pinyins = Pinyin.pinyin(text);
for (String pinyin : pinyins) {
if (StringUtils.isEmpty(pinyin)) {
continue;
}
builder.append(pinyin.substring(0,1).toUpperCase()+pinyin.substring(1));
}
return builder.toString();
}
/**
* 拼音(带数字音标)
* @param word
* @return
*/
public static String tonePinyin(String text) {
StringBuilder builder = new StringBuilder();
List<String> pinyins = Pinyin.tonePinyin(text);
for (String pinyin : pinyins) {
if (null != pinyin) {
builder.append(pinyin+" ");
}
}
return builder.toString();
}
/**
* 拼音(带符号音标)
* @param word
* @return
*/
public static String unicodePinyin(String text) {
StringBuilder builder = new StringBuilder();
List<String> pinyins = Pinyin.unicodePinyin(text);
for (String pinyin : pinyins) {
if (null != pinyin) {
builder.append(pinyin+" ");
}
}
return builder.toString();
}
/**
* 词频统计
* @param words
* @return
*/
public static Map<String, Double> wordCount(List<String> words) {
WordWeight ww = new WordWeight();
for (String word : words) {
ww.add(word);
}
return ww.export();
}
/**
* 词频统计
* @param words
* @return
*/
public static List<String> wordCount1(List<String> words) {
List<String> wcs = new ArrayList<String>();
WordWeight ww = new WordWeight();
for (String word : words) {
ww.add(word);
}
Map<String, Double> export = ww.export();
for (Entry<String, Double> entry : export.entrySet()) {
wcs.add(entry.getKey()+":"+entry.getValue());
}
return wcs;
}
/**
* 语种识别:1英文；0中文
* @param words
* @return
*/
public static int language(String word) {
return WordAlert.isEnglish(word)?1:0;
}
}
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