（转载）利用Ansj进行新闻关键词提取

1-下载JAR包

显然下载JAR包是最省事的方法，当然你也可以把Github上面的工程加进来，无奈JAR包找了好久未找到，最后只好向作者索取，目前已经上传到CSDN了，欢迎下载：http://download.csdn.net/detail/jj12345jj198999/6020541

 

2-自定义keyword类

虽然ANSJ中已经实现了关键词提取，不过输出时并没有给出每一个关键词的权重，于是只好手动修改keyword类，好在Github上面的源码中已经定义了权重成员，我们只需要增加一个Get函数即可。

public class Keyword implements Comparable<Keyword> {
    private String name;
    private double score;
    private double idf;
    private int freq;

    public Keyword(String name, int docFreq, int weight) {
        this.name = name;
        this.idf = Math.log(10000 + 10000.0 / (docFreq + 1));
        this.score = idf * weight;
        freq++;
    }
    public void updateWeight(int weight) {
        this.score += weight * idf;
        freq++;
    }
    public int compareTo(Keyword o) {
        if (this.score < o.score) {
            return 1;
        } else {
            return -1;
        }

    }
    public boolean equals(Object obj) {
        // TODO Auto-generated method stub
        if (obj instanceof Keyword) {
            Keyword k = (Keyword) obj;
            return k.name.equals(name);
        } else {
            return false;
        }
    }
    public String toString() {
        return name;
    }
    //look here ******************************************************
    public double getScore(){
        return score;
    }
    public int getFreq() {
        return freq;
    }

}

除了这个之外，我们还需要原封不动地把keywordcomputer类重抄一遍，随叫这两个类息息相关呢，要么都在JAR中要么就一起跳出来。对于这个类，实在不知道计算keyword时为何要知道title的长度，作者告诉我这不是他写的也不知道缘由，我猜Github上面添加这个模块的人肯定认为出现在标题中的词需要赋予较大的权重吧，也许这个想法是对的。

import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.TreeSet;

import org.ansj.app.newWord.LearnTool;
import org.ansj.domain.Term;
import org.ansj.recognition.NatureRecognition;
import org.ansj.splitWord.analysis.NlpAnalysis;

public class KeyWordComputer {
    private int nKeyword = 10;
    //default constructor keyword number=10
    public KeyWordComputer() {
        nKeyword = 10;
    }
    // constructor set keyword number
    public KeyWordComputer(int nKeyword) {
        this.nKeyword = nKeyword;

    }
    //get keywords object list
    private List<Keyword> computeArticleTfidf(String content, int titleLength) {
        Map<String, Keyword> tm = new HashMap<String, Keyword>();
        LearnTool learn = new LearnTool();
        List<Term> parse = NlpAnalysis.parse(content, learn);
        parse = NlpAnalysis.parse(content, learn);
        for (Term term : parse) {
            int weight = getWeight(term, content.length(), titleLength);
            if (weight == 0)
                continue;
            Keyword keyword = tm.get(term.getName());
            if (keyword == null) {
                keyword = new Keyword(term.getName(), term.getNatrue().allFrequency, weight);
                tm.put(term.getName(), keyword);
            } else {
                keyword.updateWeight(1);
            }
        }
        TreeSet<Keyword> treeSet = new TreeSet<Keyword>(tm.values());
        ArrayList<Keyword> arrayList = new ArrayList<Keyword>(treeSet);
        if (treeSet.size() < nKeyword) {
            return arrayList;
        } else {
            return arrayList.subList(0, nKeyword);
        }
    }
    //get keywords,need title and content
    public Collection<Keyword> computeArticleTfidf(String title, String content) {
        return computeArticleTfidf(title + "\t" + content, title.length());
    }
    //get keywords, just need content
    public Collection<Keyword> computeArticleTfidf(String content) {
        return computeArticleTfidf(content, 0);
    }
    //get keywords weight
    private int getWeight(Term term, int length, int titleLength) {
        if (term.getName().matches("(?s)\\d.*")) {
            return 0;
        }
        if (term.getName().trim().length() < 2) {
            return 0;
        }
        String pos = term.getNatrue().natureStr;
        if (!pos.startsWith("n") || "num".equals(pos)) {
            return 0;
        }
        int weight = 0;
        if (titleLength > term.getOffe()) {
            return 20;
        }
        // position
        double position = (term.getOffe() + 0.0) / length;
        if (position < 0.05)
            return 10;
        weight += (5 - 5 * position);
        return weight;
    }
}

 

3-test

最后就是写一个类测试，import一堆东西

import java.util.List;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.StringReader;
import java.util.Iterator;

import org.ansj.app.newWord.LearnTool;
import org.ansj.domain.Term;
import org.ansj.recognition.NatureRecognition;
import org.ansj.splitWord.Analysis;
import org.ansj.splitWord.analysis.NlpAnalysis;
import org.ansj.splitWord.analysis.ToAnalysis;
import org.ansj.util.*;
import org.ansj.recognition.*;


public class test {
    public static void main(String[] args) throws IOException {
        String filePath = "./test-utf8.TXT";
        String tt=new String();
        BufferedReader in = new BufferedReader(new InputStreamReader(new FileInputStream(filePath), "UTF8"));
        String str;
        while ((str = in.readLine()) != null) {
            tt+=str;
        }
       test1(tt);
       
       System.out.println("*************************");
       filePath = "./test1.txt";
       BufferedReader in2 = new BufferedReader(new InputStreamReader(new FileInputStream(filePath), "UTF8"));
       String str2;
       String tt2=new String();
       while ((str2 = in2.readLine()) != null) {
            tt2+=str2;
       }
       test1(tt2);
       
    }
    
    public static void test1(String content){
        KeyWordComputer key=new KeyWordComputer(10);
        Iterator it = key.computeArticleTfidf(content).iterator() ;
        while(it.hasNext()) {
            Keyword key2=(Keyword)it.next();
            System.out.println(key2.toString()+key2.getScore());    
        }  
    }
}

 

在测试时有一点需要注意，ANSJ目前只支持UTF-8格式的字符串，其他格式的运行时会报JAVA GC错误，作者说因为处理过程中是乱码，程序以为都是新词导致内存崩溃了，希望他在以后的版本中加以改进吧。最后给出输出结果，这里是按照权重排序的：

init ambiguity  waring :library/ambiguity.dic because : not find that file or can not to read !
init core library ok use time :3983
init ngram ok use time :2023
屌丝528.8693014046396
李毅202.62858475668492
网络174.9965471938941
球迷110.52413506982782
群体110.52413506982782
人人110.52413506982782
名号101.31379048067551
高富帅92.10390216212956
满屏92.10390216212954
网友92.1034458915232
*************************
社会主义1446.0241004969153
社会1326.289620837935
中国1096.0347881537828
人民1049.9792831633652
文化874.9827359694709
经济874.9827359694709
特色847.3517022020139
制度801.2999792562523
体系746.0379117213383
国家598.6723982949011

 

 

转载自：http://blog.csdn.net/zhaoxinfan/article/details/10403917 因CSDN网络问题，我们常打不开，因此转载于博客园。