主要思想:
1. 要有一个语料库
2. 统计每个词出现的频率, 一会来做朴素贝叶斯候选
3. 举例: 中国人民共和国的
其中语料库中有中国, 人民, 中国人, 共和国等等的词组.
现在输入: 中国人都爱中华人民共和国;
分词的时候取max( 各种分发得到的score );
例如: solution1：中国人_都爱中华人民_共和国
solution2：中国_人_都爱中华人民_共和国
solution3：中国_人_都爱_中华_人民_共和国
bestSegSolution = max( solutions(segSlution[i] ));
4.对于一句汉字的分词可以看做
seg( StringIn ) = firPart + seg(StringIn – firPart); // 我用score来衡量当前分词结果的好坏
6。 朴素贝叶斯的意思就是: 分词后的, 两个词之间是相互独立的, 也就是后者的出现与前者无关
5. 这个只是初级版, 很简单, 需要再加点东西, 结果会更加的完美.. 当然, 按照做事情的原则, 都是从简单开始做的, 再努力
using System; using System.Collections.Generic; using System.Text; using System.Collections; using System.Windows.Forms; using System.IO; using System.Diagnostics; namespace ChineseWordSeg { class NaiveBayes { private string wordLibPath = "../WordLib/pku_training.txt";//所用的训练库是pku的语料库.
bool trained = false; private Dictionary<string, long> wordLib = new Dictionary<string, long>(); private Dictionary<string, long> singleWordLib = new Dictionary<string, long>(); int maxLen = 0; long maxScore = 0; private string segPos = " "; //记录单句的分割点, 按照标点等非汉字的字符分开 private string segSentence = " "; // 记录整个段落的 // 是不是中文字符
bool isChineseWord(char chr ){ if (chr >= 0x4E00 && chr <= 0x9FFF) return true; return false; } public void trainDate( string path ) { // 统计每个词出现的次数
//1. 统计每个词组频率, naiveBayes消歧. 将一个组合不同的方式取得较大概率的那个分组方式.
// 难道每个词还是hash一下么?
//2. 统计每个字的频率, 就像向心力那样... 看看到底哪两个字比较容易联系到一起 这个是一句废话,因为我没这么去做
wordLib.Clear();
DirectoryInfo dirInfo = new DirectoryInfo(path);
DirectoryInfo tmpDir = dirInfo.Parent;
string savePath = tmpDir.FullName;
FileInfo fInfo = new FileInfo(wordLibPath);
string fileNamePre = fInfo.Name;
savePath += "\\" + fileNamePre + "_trained";
FileInfo infoOfDB = new FileInfo(savePath);
if( File.Exists(savePath) && infoOfDB.Length > 0 ){
StreamReader sr1 =
new StreamReader(@savePath);
char[] sep = { ' '};
while (sr1.Peek()!=-1)
{
string[] keyValue = sr1.ReadLine().Split(sep);
wordLib[keyValue[0]] = Convert.ToInt32(keyValue[1]);
}
return;
}
if ( !File.Exists( path ) ) {
MessageBox.Show("ÓïÁÏ¿â·¾¶ÓÐ´í£¬Çë¼ì²é");
return;
}
Stopwatch tm = new Stopwatch();
tm.Start();
StreamReader sr =
new StreamReader(@path,
System.Text.Encoding.GetEncoding("gb2312"));
char tmpChar;
string tmpStr;
char[] tmpCArray = new char[100];
{
tmpStr = " ";
bool flag = false;
long tmpVal = 0;
while (sr.Peek() != -1 ) {
tmpChar = (char)sr.Read();
if (isChineseWord( tmpChar ) )
{
flag = true;
/*
if (flag == true)
{
string singleWord = (tmpChar).ToString();
if (singleWordLib.ContainsKey(singleWord))
{
singleWordLib.TryGetValue(singleWord, out tmpVal);
singleWordLib[singleWord] = tmpVal + 1;
}
else
singleWordLib.Add(singleWord, 1);
// ͳ¼Æÿ¸ö×ÖµÄ
}*/
tmpStr += (char)tmpChar;
}
else
{
tmpStr = tmpStr.Trim();
if (flag == true)
{
if( tmpStr.Length > 1 ){
if (wordLib.ContainsKey(tmpStr))
{
wordLib.TryGetValue(tmpStr, out tmpVal);
wordLib[tmpStr]=tmpVal + 1;
}
else
wordLib.Add(tmpStr, 1);
}
else{
if (singleWordLib.ContainsKey(tmpStr))
{
singleWordLib.TryGetValue(tmpStr, out tmpVal);
singleWordLib[tmpStr] = tmpVal + 1;
}
else
singleWordLib.Add(tmpStr, 1);
}
// ͳ¼Æÿ¸ö´Ê×éµÄ
}
tmpStr = " ";
flag = false;
}
if (maxLen < tmpStr.Length)
{
maxLen = tmpStr.Length;
// ¼Ç¼µ¥´Ê×î´óµÄ³¤¶È...
}
}
}
sr.Close();
StreamWriter sw = new StreamWriter(savePath);
foreach ( string key in wordLib.Keys ) {
sw.WriteLine( key + " " + wordLib[key]);
}
sw.Close();
tm.Stop();
MessageBox.Show(tm.Elapsed.Milliseconds.ToString(), "training done");
}
//将分段好的结果传回.
public string getSegedString( string strIn ) {
char[] seprator = { 's' };
string[] segSplit = segSentence.Split(seprator);
List<int> segP = new List<int>();
segP.Clear();
int j, i;
int cntSegPos = 0;
for( i = 0; i < segSplit.Length; i ++ ){
if (segSplit[i].Length > 0)
{
segP.Add(Convert.ToInt16(segSplit[i]));
cntSegPos++;
}
}
char[] cArray = new char[512];
cArray = strIn.ToCharArray();
string strOut = " ";
bool flag = true;
for (i = 0, j = 0; i < strIn.Length; i++)
{
while (j < cntSegPos && segP.Contains(i))
{
segP.Remove(i);
flag = !flag;
if (flag)
strOut += ")";
else strOut += "(";
j++;
}
strOut += cArray[i];
}
if (j < cntSegPos) strOut += ")";
return strOut;
}
// 恩, 做朴素贝叶斯分词
public string doNaiveBayesSegmentation(string strIn, string trainDataPath){
if( !trained )
{
trained = true;
trainDate(trainDataPath);
}
string strTmp = " ";
char[] charBuffer = new char[4096];
charBuffer = strIn.ToCharArray();
int i = 0, len = strIn.Length;
while ( i < len )
{
while ( i < len && isChineseWord(charBuffer[i]) ) strTmp += charBuffer[i++];
{
if(strTmp.Length > 0)
{
maxScore = 0;
segPos = " ";
naviveBayesSeg(strTmp, 0, "", i-strTmp.Length);
segSentence += segPos;
}
strTmp = "";
}
while (i < len && !isChineseWord(charBuffer[i])) i++;
}
return getSegedString(strIn);
}
// 分词的具体实现, bestSegSolution = max( solutions(segSlution[i] ));
对于一句汉字的分词可以看做 seg( StringIn ) = firPart + seg(StringIn – firPart);
我用score来衡量当前分词结果的好坏
public void naviveBayesSeg(string strIn, long score, string seg, int tPos){
if ( true ) {
if( score > maxScore ) {
segPos = seg;
maxScore = score;
}
// return;
}
int strLen = strIn.Length;
string firStr = "";
int i = 0;
for ( i = 1; i <= strIn.Length; i++) {
firStr = strIn.Substring(0, i);
if (wordLib.ContainsKey(firStr))
{
naviveBayesSeg(strIn.Substring(i), score + wordLib[firStr], seg + (tPos + i - firStr.Length).ToString() + "s" + (tPos + i).ToString()+"s", tPos + i);
}
}
if( i > strIn.Length && i > 1)
naviveBayesSeg(strIn.Substring(1), score, seg, tPos + 1);
}
}
}