代码改变世界

贝叶斯公式与拼写检查器

2011-12-27 22:34  htc开发  阅读(432)  评论(0编辑  收藏  举报

年底工作不是很忙,今天复习了下概率论中贝叶斯的基础知识,动手写了个Java版本的简单的拼写检查器。
我们在使用Google时,当我们输入一个错误的单词,经常可以看到Google提示我们是不是要查找什么什么。
它是怎样做到的呢?现在我们就来实现一个简单的拼写检查器。


1. 什么是贝叶斯公式?

来看来自维基百科的定义:

贝叶斯定理

贝叶斯定理由英国数学家贝叶斯 ( Thomas Bayes 1702-1761 ) 发展,用来描述两个条件概率之间的关系,比如 P(A|B) 和 P(B|A)。按照定理 6 的乘法法则,P(A∩B)=P(A)·P(B|A)=P(B)·P(A|B),可以立刻导出贝叶斯定理:


如上公式也可变形为 

另一个例子,现分别有 AB 两个容器,在容器 A 里分别有 7 个红球和 3 个白球,在容器 B 里有 1 个红球和 9 个白球,现已知从这两个容器里任意抽出了一个球,且是红球,问这个红球是来自容器 A 的概率是多少?

假设已经抽出红球为事件 B,从容器 A 里抽出球为事件 A,则有:P(B) = 8 / 20,P(A) = 1 / 2,P(B | A) = 7 / 10,按照公式,则有:


在上面的例子中,
事件A:要猜测事件的概率(从容器A里抽出球 - 要猜测和计算的事件)
事件B:现实已发生事件的概率(抽出红球 - 已经抽出红球,已经发生的事件)

正因贝叶斯公式可用于事件发生概率的推测,因此它广泛应用于计算机领域。从垃圾邮件的过滤,中文分词,机器翻译等等。下面的拼写检查器可以说是牛刀小试了。


2. 拼写检查器

第一步,以一个比较大的文本文件big.txt作为样本,分析每个单词出现的概率作为语言模型(Language Model)和词典。
big.txt的地址是:http://norvig.com/big.txt

第二步,如果用户输入的单词不在词典中,则产生编辑距离(Edit Distance)为2的所有可能单词。所谓编辑距离为1就是对用户输入的单词进行删除1个字符、添加1个字符、交换相邻字符、替换1个字符产生的所有单词。而编辑距离为2就是对这些单词再进行一次上述所有变换,因此最后产生的单词集会很大。可以与词典作差集,只保留词典中存在的单词。

第三步,假设事件c是我们猜测用户可能想要输入的单词,而事件w是用户实际输入的错误单词,根据贝叶斯公式可知:
     P(c|w) = P(w|c) * P(c) / P(w)。
这里的P(w)对于每个单词都是一样的,可以忽略。而P(w|c)是误差模型(Error Model),是用户想要输入w却输入c的概率,这是需要大量样本数据和事实依据来得到的,为了简单起见也忽略掉。因此,我们可以找出编辑距离为2的单词集中P(c)概率最大的几个来提示用户。


3. Java代码实现

package com.cdai.studio.spellcheck;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Set;
import java.util.regex.Pattern;

public class SpellCheck {

       private static final char[] ALPHABET = "abcdefghijklmnopqrstuvwxyz".toCharArray();
       
       public static void main(String[] args) throws Exception {
              new SpellCheck().start();
       }
       
       public void start() throws IOException {
              // 1.Build language model
              Map<String, Double> langModel = buildLanguageModel("big.txt");
              Set<String> dictionary = langModel.keySet();
              
              // 2.Read user input loop
              BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));
              String input;
              while ((input = reader.readLine()) != null) {
                     input = input.trim().toLowerCase();
                     if ("bye".equals(input))
                           break;
                     if (dictionary.contains(input))
                           continue;
                     long startTime = System.currentTimeMillis();
                     
                     // 3.Build set for word in edit distance and remove inexistent in dictionary
                     Set<String> wordsInEditDistance2 = buildEditDistance2Set(langModel, input);
                     wordsInEditDistance2.retainAll(dictionary);
                     
                     // 4.Calculate Bayes's probability
                     // c - correct word we guess, w - wrong word user input in reality
                     // argmax P(c|w) = argmax P(w|c) * P(c) / P(w)
                     // we ignore P(w) here, because it's the same for all words
                     List<String> guessWords = guessCorrectWord(langModel, wordsInEditDistance2);
                     System.out.printf("Do you mean %s ? Cost time: %.3f second(s)\n",
                                  guessWords.toString(), (System.currentTimeMillis() - startTime) / 1000D);
              }
              
       }
       
       private Map<String, Double> buildLanguageModel(String sample)
                     throws IOException {
              Map<String, Double> langModel = new HashMap<String, Double>();
              BufferedReader reader = new BufferedReader(new FileReader(sample));
              Pattern pattern = Pattern.compile("[a-zA-Z]+");
              String line;
              int totalCnt = 0;
              while ((line = reader.readLine()) != null) {
                     String[] words = line.split(" ");
                     for (String word : words) {
                           if (pattern.matcher(word).matches()) {
                                  word = word.toLowerCase();
                                  Double wordCnt = langModel.get(word);
                                  if (wordCnt == null)
                                         langModel.put(word, 1D);
                                  else
                                         langModel.put(word, wordCnt + 1D);
                                  totalCnt++;
                           }
                     }
              }
              reader.close();
              
              for (Entry<String, Double> entry : langModel.entrySet())
                     entry.setValue(entry.getValue() / totalCnt);
              
              return langModel;
       }
       
       private Set<String> buildEditDistance1Set(
                     Map<String, Double> langModel,
                     String input) {
              Set<String> wordsInEditDistance = new HashSet<String>();
              char[] characters = input.toCharArray();
              
              // Deletion: delete letter[i]
              for (int i = 0; i < input.length(); i++)
                     wordsInEditDistance.add(input.substring(0,i) + input.substring(i+1));
              
              // Transposition: swap letter[i] and letter[i+1]
              for (int i = 0; i < input.length()-1; i++)
                     wordsInEditDistance.add(input.substring(0,i) + characters[i+1] +
                                  characters[i] + input.substring(i+2));
              
              // Alteration: change letter[i] to a-z
              for (int i = 0; i < input.length(); i++)
                     for (char c : ALPHABET)
                           wordsInEditDistance.add(input.substring(0,i) + c + input.substring(i+1));
              
              // Insertion: insert new letter a-z
              for (int i = 0; i < input.length()+1; i++)
                     for (char c : ALPHABET)
                           wordsInEditDistance.add(input.substring(0,i) + c + input.substring(i));
              
              return wordsInEditDistance;
       }
       
       private Set<String> buildEditDistance2Set(
                     Map<String, Double> langModel,
                     String input) {
              Set<String> wordsInEditDistance1 = buildEditDistance1Set(langModel, input);
              Set<String> wordsInEditDistance2 = new HashSet<String>();
              for (String editDistance1 : wordsInEditDistance1)
                     wordsInEditDistance2.addAll(buildEditDistance1Set(langModel, editDistance1));
              wordsInEditDistance2.addAll(wordsInEditDistance1);
              return wordsInEditDistance2;
       }
       
       private List<String> guessCorrectWord(
                     final Map<String, Double> langModel,
                     Set<String> wordsInEditDistance) {
              List<String> words = new LinkedList<String>(wordsInEditDistance);
              Collections.sort(words, new Comparator<String>() {
                     @Override
                     public int compare(String word1, String word2) {
                           return langModel.get(word2).compareTo(langModel.get(word1));
                     }
              });
              return words.size() > 5 ? words.subList(0, 5) : words;
       }
       
}

运行结果:

raechel
Do you mean [reached, reaches, ranches, rachel] ? Cost time: 0.219 second(s)
thew
Do you mean [the, that, he, her, they] ? Cost time: 0.062 second(s)


虽然不是很准确,但是不是很有趣呢?如果感兴趣,我们可以继续深入学习。
有了兴趣和求知欲,并不断实践,才能学好编程。


2011.12.28更新:

产生编辑距离为2的单词时,应该让编辑距离为1的单词具有更高的优先级。并且当用户输入的单词长度较长时,产生编辑距离为2的单词可能会花费一些时间。所以可以优化为首先产生编辑距离为1的单词,如果与词典做差集后为空,再产生编辑距离为2的单词。将main方法中的第三步代码修改为:

                    // 3.Build set for word in edit distance and remove inexistent in dictionary
                     Set<String> wordsInEditDistance = buildEditDistance1Set(langModel, input);
                     wordsInEditDistance.retainAll(dictionary);
                     if (wordsInEditDistance.isEmpty()) {
                           wordsInEditDistance = buildEditDistance2Set(langModel, input);
                           wordsInEditDistance.retainAll(dictionary);
                           if (wordsInEditDistance.isEmpty()) {
                                  System.out.println("Failed to check this spell");
                                  continue;
                           }
                     }



参考文章

怎样写一个拼写检查器 http://blog.youxu.info/spell-correct.html