Java calls the IK tokenizer for word segmentation and encapsulates tool classes

Table of contents

scenes to be used

import dependencies

use of participle

Packaging tools

---

scenes to be used

        In the big data scenario, it is generally used to count the frequency of keywords, so we need to segment some data texts to get the keywords we want.

import dependencies

<dependency>
            <groupId>com.janeluo</groupId>
            <artifactId>ikanalyzer</artifactId>
            <version>2012_u6</version>
        </dependency>

use of participle

ArrayList<String> result = new ArrayList<>();
// 创建一个reader对象
StringReader reader = new StringReader(keyword);
// 创建一个分词对象
IKSegmenter ikSegmenter = new IKSegmenter(reader, true);
Lexeme next = ikSegmenter.next();

while ( next != null ) {
      // 获取分词的结果
      result.add(next.getLexemeText());
      next = ikSegmenter.next();
}
return result;

word segmentation result

The result when useSmart = true, a word will not be repeated

 The result when useSmart = false, the word will appear multiple times

 Obviously useSmart = false works better

Packaging tools

package com.cw.util;

import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;

import java.io.IOException;
import java.io.StringReader;
import java.util.ArrayList;
import java.util.List;

/**
 * @author CW
 * @version 1.0
 * @date 2023/3/1 8:41
 * @desc ik分词工具类
 */
public class IKUtil {

    /**
     *  分词
     * @param keyword 需要分词的文本
     * @return
     */
    public static List<String> splitKeyWord(String keyword) throws IOException {

        ArrayList<String> result = new ArrayList<>();
        // 创建一个reader对象
        StringReader reader = new StringReader(keyword);
        // 创建一个分词对象
        IKSegmenter ikSegmenter = new IKSegmenter(reader, false);
        Lexeme next = ikSegmenter.next();

        while ( next != null ) {
            // 获取分词的结果
            result.add(next.getLexemeText());
            next = ikSegmenter.next();
        }
        return result;
    }
}