Word Co-occurrence一直不知道该怎么正确翻译, 单词相似度 还是共生单词 还是单词的共生矩阵
这在统计里面是很常用的文本处理算法,用来度量一组文档集中所有出现频率最接近的词组.嗯,其实是上下文词组,不是单词.算是一个比较常用的算法,可以衍生出其他的统计算法.能用来做推荐,因为它能够提供的结果是"人们看了这个,也会看那个".比如做一些协同过滤之外的购物商品的推荐,信用卡的风险分析,或者是计算大家都喜欢什么东西.
比如 I love you , 出现 "I love" 的同时往往伴随着 "love you" 的出现,不过中文的处理跟英文不一样,需要先用分词库做预处理.
按照Mapper, Reducer和Driver的方式拆分代码
Mapper程序:
package wco;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WCoMapper extends Mapper {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
/*
* 将行内容全部转换为小写格式.
*/
String line_lc = value.toString().toLowerCase();
String before = null;
/*
* 将行拆分成单词
* 并且key是前一个单词加上后一个单词
* value 是 1
*/
for (String word : line_lc.split("\\W+")) { //循环行内容,按照空格进行分割单词
if (word.length() > 0) {
if (before != null) { //如果前词不为空,则写入上下文(第一次前词一定是空,直接跳到下面的before = word)
context.write(new Text(before + "," + word), new IntWritable(1));
}
before = word; //将现词赋值给前词
}
}
}
}
Reducer程序:
package wco;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WCoReducer extends Reducer {
@Override
public void reduce(Text key, Iterable values, Context context)
throws IOException, InterruptedException {
int wordCount = 0;
for (IntWritable value : values) {
wordCount += value.get(); //单纯计算word count
}
context.write(key, new IntWritable(wordCount));
}
}
Driver程序就不解释了，天下的Driver都一样:
package wco;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class WCo extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.out.printf("Usage: hadoop jar wco.WCo
return -1;
}
Job job = new Job(getConf());
job.setJarByClass(WCo.class);
job.setJobName("Word Co Occurrence");
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(WCoMapper.class);
job.setReducerClass(WCoReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
boolean success = job.waitForCompletion(true);
return success 0 : 1;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new Configuration(), new WCo(), args);
System.exit(exitCode);
}
}
算法的核心其实就是把前词和后词同时取出来作为key加上一个value做word count,统计单词的共生频率来对文本进行聚类.看网上说k-means的很多,其实很多时候算法是根据需求走的,k-means或者模糊k均值不一定就高大上,wordcount也不一定就穷矮矬.
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