public static void TestBiGraphGenerate()
{
WordDictionary coreDict = new WordDictionary();
if (!coreDict.Load(coreDictFile))
{
Console.WriteLine("coreDict 字典装入错误！");
return;
}
WordDictionary biDict = new WordDictionary();
if (!biDict.Load(biDictFile))
{
Console.WriteLine("字典装入错误！");
return;
}
string sSentence = @"他说的确实在理";
sSentence = Predefine.SENTENCE_BEGIN + sSentence + Predefine.SENTENCE_END;
//---原子分词
List<AtomNode> atomSegment = Segment.AtomSegment(sSentence);
//---检索词库，加入所有可能分词方案并存入链表结构
RowFirstDynamicArray<ChainContent> segGraph = Segment.GenerateWordNet(atomSegment, coreDict);
//---检索所有可能的两两组合
ColumnFirstDynamicArray<ChainContent> biGraphResult = Segment.BiGraphGenerate(segGraph, 0.1, biDict, coreDict);
Console.WriteLine(biGraphResult.ToString());
}
public Segment(WordDictionary biDict, WordDictionary coreDict)
{
this.biDict = biDict;
this.coreDict = coreDict;
rawNShortPath = new NShortPath();
optNShortPath = new NShortPath();
}
public WordSegment(double SmoothingParameter)
{
m_dictCore = new WordDictionary();
m_dictBigram = new WordDictionary();
m_POSTagger = new Span();
m_uPerson = new UnknowWord();
m_uTransPerson = new UnknowWord();
m_uPlace = new UnknowWord();
m_Seg = new Segment(m_dictBigram, m_dictCore);
m_Seg.OnSegmentEvent += new SegmentEventHandler(this.OnSegmentEventHandler);
m_dSmoothingPara = SmoothingParameter;//Smoothing parameter
}
//Unknown word recognition
//pWordSegResult:word Segmentation result;
//graphOptimum: The optimized segmentation graph
//graphSeg: The original segmentation graph
public bool Recognition(WordResult[] pWordSegResult, RowFirstDynamicArray<ChainContent> graphOptimum,
List<AtomNode> atomSegment, WordDictionary dictCore)
{
ChainItem<ChainContent> item;
int nStartPos = 0, j = 0, nAtomStart, nAtomEnd, nPOSOriginal;
double dValue;
m_roleTag.POSTagging(pWordSegResult, dictCore, m_dict);
//Tag the segmentation with unknown recognition roles according the core dictionary and unknown recognition dictionary
for (int i = 0; i < m_roleTag.m_nUnknownWordsCount; i++)
{
while (j < atomSegment.Count && nStartPos < m_roleTag.m_nUnknownWords[i, 0])
nStartPos += atomSegment[j++].sWord.Length;
nAtomStart = j;
while (j < atomSegment.Count && nStartPos < m_roleTag.m_nUnknownWords[i, 1])
nStartPos += atomSegment[j++].sWord.Length;
nAtomEnd = j;
if (nAtomStart < nAtomEnd)
{
item = graphOptimum.GetElement(nAtomStart, nAtomEnd);
if (item != null)
{
dValue = item.Content.eWeight;
nPOSOriginal = item.Content.nPOS;
}
else
dValue = Predefine.INFINITE_VALUE;
if (dValue > m_roleTag.m_dWordsPossibility[i])
//Set the element with less frequency
graphOptimum.SetElement(nAtomStart, nAtomEnd, new ChainContent(m_roleTag.m_dWordsPossibility[i], m_nPOS, m_sUnknownFlags));
}
}
return true;
}
//====================================================================
// ����������֮��Ķ���ͼ��
//====================================================================
public static ColumnFirstDynamicArray<ChainContent> BiGraphGenerate(
RowFirstDynamicArray<ChainContent> aWord, double smoothPara, WordDictionary biDict, WordDictionary coreDict)
{
ColumnFirstDynamicArray<ChainContent> aBiWordNet = new ColumnFirstDynamicArray<ChainContent>();
ChainItem<ChainContent> pCur, pNextWords;
int nTwoWordsFreq = 0, nCurWordIndex, nNextWordIndex;
double dCurFreqency, dValue, dTemp;
string sTwoWords;
StringBuilder sb = new StringBuilder();
//Record the position map of possible words
int[] m_npWordPosMapTable = PreparePositionMap(aWord);
pCur = aWord.GetHead();
while (pCur != null)
{
if (pCur.Content.nPOS >= 0)
//It's not an unknown words
dCurFreqency = pCur.Content.eWeight;
else
//Unknown words
dCurFreqency = coreDict.GetFrequency(pCur.Content.sWord, 2);
//Get next words which begin with pCur.col��ע��������Ķ�Ӧ��ϵ��
pNextWords = aWord.GetFirstElementOfRow(pCur.col);
while (pNextWords != null && pNextWords.row == pCur.col)
{
sb.Remove(0, sb.Length);
sb.Append(pCur.Content.sWord);
sb.Append(Predefine.WORD_SEGMENTER);
sb.Append(pNextWords.Content.sWord);
sTwoWords = sb.ToString();
//Two linked Words frequency
nTwoWordsFreq = biDict.GetFrequency(sTwoWords, 3);
//Smoothing
dTemp = 1.0 / Predefine.MAX_FREQUENCE;
//-log{a*P(Ci-1)+(1-a)P(Ci|Ci-1)} Note 0<a<1
dValue = -Math.Log(smoothPara * (1.0 + dCurFreqency) / (Predefine.MAX_FREQUENCE + 80000.0)
+ (1.0 - smoothPara) * ((1.0 - dTemp) * nTwoWordsFreq / (1.0 + dCurFreqency) +
dTemp));
//Unknown words: P(Wi|Ci);while known words:1
if (pCur.Content.nPOS < 0)
dValue += pCur.Content.nPOS;
//Get the position index of current word in the position map table
nCurWordIndex = Utility.BinarySearch(pCur.row * Predefine.MAX_SENTENCE_LEN + pCur.col, m_npWordPosMapTable);
nNextWordIndex = Utility.BinarySearch(pNextWords.row * Predefine.MAX_SENTENCE_LEN + pNextWords.col, m_npWordPosMapTable);
aBiWordNet.SetElement(nCurWordIndex, nNextWordIndex, new ChainContent(dValue, pCur.Content.nPOS, sTwoWords));
pNextWords = pNextWords.next; //Get next word
}
pCur = pCur.next;
}
return aBiWordNet;
}
/// <summary>
/// 导入外部词库，词频按照重合词频比例平均值
/// </summary>
/// <param name="ImportDicFile">外部词库文件名</param>
/// <param name="ImportEncoding">外部词库文件编码</param>
/// <param name="SourceDicFile">源dct文件名</param>
/// <param name="DestDicFile">目标dct文件名</param>
/// <param name="DicFormat">外部词库类型</param>
/// <param name="OddLines">导入的库中无效且不在源库中的数据</param>
/// <param name="ImportFrqRate">设置固定的导入文件频度比例（除以此数字后入库,小于等于0则按照AvgFrqRate入库）</param>
/// <param name="AvgFrqRate">导入文件的平均频度比例</param>
/// <returns>导入的条数</returns>
public static int ImportDictionary(string ImportDicFile, Encoding ImportEncoding, string SourceDicFile, string DestDicFile, DictionaryFormat DicFormat, out string[] OddLines, out double AvgFrqRate, double ImportFrqRate = 0)
{
//初始化
double MaxFrqRate, MinFrqRate;
WordDictionary.DicWordInfo[] NewWords;
WordDictionary.DicWordInfo[] ExistWords;
FindDifferent(ImportDicFile, ImportEncoding, DicFormat, SourceDicFile, out OddLines, out NewWords, out ExistWords, out MaxFrqRate, out MinFrqRate, out AvgFrqRate);
//加载词库
WordDictionary dict = new WordDictionary();
if (!dict.Load(SourceDicFile))
throw new Exception("load source dic file fail");
//加入新词
foreach (WordDictionary.DicWordInfo Word in NewWords)
{
int Frq = Convert.ToInt32(ImportFrqRate <= 0 ? Word.Frequence / AvgFrqRate : Word.Frequence / ImportFrqRate);
dict.AddWord(Word.Word, Word.Pos, Frq);
}
//保存
dict.Save(DestDicFile);
dict.ReleaseDict();
return NewWords.Length;
}
//====================================================================
//Merge dict2 into current dictionary and the frequency ratio from dict2 and current dict is nRatio
//====================================================================
public bool Merge(WordDictionary dict2, int nRatio)
{
int i, j, k, nCmpValue;
string sWord;
//Modification made, not to output when modify table exists.
if (modifyTable != null || dict2.modifyTable != null)
{
return(false);
}
for (i = 0; i < Predefine.CC_NUM; i++)
{
j = 0;
k = 0;
while (j < indexTable[i].nCount && k < dict2.indexTable[i].nCount)
{
nCmpValue = Utility.CCStringCompare(indexTable[i].WordItems[j].sWord, dict2.indexTable[i].WordItems[k].sWord);
if (nCmpValue == 0)
//Same Words and determine the different handle
{
if (indexTable[i].WordItems[j].nPOS < dict2.indexTable[i].WordItems[k].nPOS)
{
nCmpValue = -1;
}
else if (indexTable[i].WordItems[j].nPOS > dict2.indexTable[i].WordItems[k].nPOS)
{
nCmpValue = 1;
}
}
if (nCmpValue == 0)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency + dict2.indexTable[i].WordItems[k].nFrequency) / (nRatio + 1);
j += 1;
k += 1;
}
//Get next word in the current dictionary
else if (nCmpValue < 0)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency) / (nRatio + 1);
j += 1;
}
else
//Get next word in the second dictionary
{
if (dict2.indexTable[i].WordItems[k].nFrequency > (nRatio + 1) / 10)
{
sWord = string.Format("{0}{1}", Utility.CC_ID2Char(i).ToString(), dict2.indexTable[i].WordItems[k].sWord);
AddItem(sWord, dict2.indexTable[i].WordItems[k].nPOS, dict2.indexTable[i].WordItems[k].nFrequency / (nRatio + 1));
}
k += 1;
}
}
//words in current dictionary are left
while (j < indexTable[i].nCount)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency) / (nRatio + 1);
j += 1;
}
//words in Dict2 are left
while (k < dict2.indexTable[i].nCount)
{
if (dict2.indexTable[i].WordItems[k].nFrequency > (nRatio + 1) / 10)
{
sWord = string.Format("{0}{1}", Utility.CC_ID2Char(i).ToString(), dict2.indexTable[i].WordItems[k].sWord);
AddItem(sWord, dict2.indexTable[i].WordItems[k].nPOS, dict2.indexTable[i].WordItems[k].nFrequency / (nRatio + 1));
}
k += 1;
}
}
return(true);
}
public bool PlaceRecognize(WordDictionary dictCore, WordDictionary placeDict)
{
int nStart = 1, nEnd = 1, i = 1, nTemp;
double dPanelty = 1.0; //Panelty value
while (m_nBestTag[i] > -1)
{
if (m_nBestTag[i] == 1)
//1 Trigger the recognition procession
{
nStart = i;
nEnd = nStart + 1;
//=========== by zhenyulu: 此处nEnd = nStart + 1;有些强迫之嫌，因此后面处理了一下
while (m_nBestTag[nEnd] == 1)
//
{
if (nEnd > nStart + 1)
dPanelty += 1.0;
nEnd++;
}
while (m_nBestTag[nEnd] == 2)
//2,12,22
nEnd++;
nTemp = nEnd;
while (m_nBestTag[nEnd] == 3)
{
if (nEnd > nTemp)
dPanelty += 1.0;
nEnd++;
}
}
else if (m_nBestTag[i] == 2)
//1,11,21 Trigger the recognition
{
dPanelty += 1.0;
nStart = i;
nEnd = nStart + 1;
while (m_nBestTag[nEnd] == 2)
//2
nEnd++;
nTemp = nEnd;
while (m_nBestTag[nEnd] == 3)
//2
{
if (nEnd > nTemp)
dPanelty += 1.0;
nEnd++;
}
}
if (nEnd > nStart)
{
//=========== by zhenyulu: 避免上面强迫之嫌带来的负面影响
if (m_sWords[nEnd] == null)
nEnd--;
m_nUnknownWords[m_nUnknownWordsCount, 0] = m_nWordPosition[nStart];
m_nUnknownWords[m_nUnknownWordsCount, 1] = m_nWordPosition[nEnd];
m_dWordsPossibility[m_nUnknownWordsCount++] = ComputePossibility(nStart, nEnd - nStart + 1, placeDict) +
Math.Log(dPanelty);
nStart = nEnd;
}
if (i < nEnd)
i = nEnd;
else
i = i + 1;
}
return true;
}
private int GetFrom(WordResult[] pWordItems, int nIndex, WordDictionary dictCore, WordDictionary dictUnknown)
{
WordInfo info;
int[] aPOS = new int[Predefine.MAX_POS_PER_WORD];
int[] aFreq = new int[Predefine.MAX_POS_PER_WORD];
int nFreq = 0, j, nRetPos = 0, nWordsIndex = 0;
bool bSplit = false; //Need to split in Transliteration recognition
int i = 1, nPOSCount;
string sCurWord; //Current word
nWordsIndex = i + nIndex - 1;
for (i = 1; i < Predefine.MAX_WORDS_PER_SENTENCE && nWordsIndex < pWordItems.Length; i++)
{
if (m_tagType == TAG_TYPE.TT_NORMAL || !dictUnknown.IsExist(pWordItems[nWordsIndex].sWord, 44))
{
m_sWords[i] = pWordItems[nWordsIndex].sWord; //store current word
m_nWordPosition[i + 1] = m_nWordPosition[i] + m_sWords[i].Length;
}
else
{
if (!bSplit)
{
m_sWords[i] = pWordItems[nWordsIndex].sWord.Substring(0, 1);
//store current word
bSplit = true;
}
else
{
m_sWords[i] = pWordItems[nWordsIndex].sWord.Substring(1);
//store current word
bSplit = false;
}
m_nWordPosition[i + 1] = m_nWordPosition[i] + m_sWords[i].Length;
}
//Record the position of current word
m_nStartPos = m_nWordPosition[i + 1];
//Move the Start POS to the ending
if (m_tagType != TAG_TYPE.TT_NORMAL)
{
//Get the POSs from the unknown recognition dictionary
sCurWord = m_sWords[i];
if (m_tagType == TAG_TYPE.TT_TRANS_PERSON && i > 0 && m_sWords[i - 1] != null &&
Utility.charType(m_sWords[i - 1].ToCharArray()[0]) == Predefine.CT_CHINESE)
{
if (m_sWords[i] == ".")
sCurWord = "．";
else if (m_sWords[i] == "-")
sCurWord = "－";
}
info = dictUnknown.GetWordInfo(sCurWord);
if (info != null)
{
nPOSCount = info.Count + 1;
for (j = 0; j < info.Count; j++)
{
//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i, j] = info.POSs[j];
m_dFrequency[i, j] = -Math.Log((double)(1 + info.Frequencies[j])) +
Math.Log((double)(m_context.GetFrequency(0, info.POSs[j]) + nPOSCount));
}
}
else
{
nPOSCount = 1;
j = 0;
}
//Get the POS set of sCurWord in the core dictionary
//We ignore the POS in the core dictionary and recognize them as other (0).
//We add their frequency to get the possibility as POS 0
if (string.Compare(m_sWords[i], "始##始") == 0)
{
m_nTags[i, j] = 100;
m_dFrequency[i, j] = 0;
j++;
}
else if (string.Compare(m_sWords[i], "末##末") == 0)
{
m_nTags[i, j] = 101;
m_dFrequency[i, j] = 0;
j++;
}
else
{
//dictCore.GetHandle(m_sWords[i], &nCount, aPOS, aFreq);
info = dictCore.GetWordInfo(m_sWords[i]);
nFreq = 0;
if (info != null)
{
for (int k = 0; k < info.Count; k++)
{
nFreq += info.Frequencies[k];
}
if (info.Count > 0)
{
m_nTags[i, j] = 0;
//m_dFrequency[i][j]=(double)(1+nFreq)/(double)(m_context.GetFrequency(0,0)+1);
m_dFrequency[i, j] = -Math.Log((double)(1 + nFreq)) + Math.Log((double)(m_context.GetFrequency(0, 0) + nPOSCount));
j++;
}
}
}
}
else
//For normal POS tagging
{
j = 0;
//Get the POSs from the unknown recognition dictionary
if (pWordItems[nWordsIndex].nPOS > 0)
{
//The word has is only one POS value
//We have record its POS and nFrequncy in the items.
m_nTags[i, j] = pWordItems[nWordsIndex].nPOS;
m_dFrequency[i, j] = -Math.Log(pWordItems[nWordsIndex].dValue) + Math.Log((double)(m_context.GetFrequency(0, m_nTags[i, j]) + 1));
if (m_dFrequency[i, j] < 0)
//Not permit the value less than 0
m_dFrequency[i, j] = 0;
j++;
}
else
{
//The word has multiple POSs, we should retrieve the information from Core Dictionary
if (pWordItems[nWordsIndex].nPOS < 0)
{
//The word has is only one POS value
//We have record its POS and nFrequncy in the items.
m_nTags[i, j] = -pWordItems[nWordsIndex].nPOS;
m_dFrequency[i, j++] = pWordItems[nWordsIndex].dValue;
}
//dictCore.GetHandle(m_sWords[i], &nCount, aPOS, aFreq);
info = dictCore.GetWordInfo(m_sWords[i]);
if (info != null)
{
nPOSCount = info.Count;
for (; j < info.Count; j++)
{
//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i, j] = info.POSs[j];
m_dFrequency[i, j] = -Math.Log(1 + info.Frequencies[j]) + Math.Log(m_context.GetFrequency(0, m_nTags[i, j]) + nPOSCount);
}
}
}
}
if (j == 0)
{
//We donot know the POS, so we have to guess them according lexical knowledge
GuessPOS(i, out j); //Guess the POS of current word
}
m_nTags[i, j] = -1; //Set the ending POS
if (j == 1 && m_nTags[i, j] != Predefine.CT_SENTENCE_BEGIN)
//No ambuguity
{
//No ambuguity, so we can break from the loop
i++;
m_sWords[i] = null;
break;
}
if (!bSplit)
nWordsIndex++;
}
if (nWordsIndex == pWordItems.Length)
nRetPos = -1;
//Reaching ending
if (m_nTags[i - 1, 1] != -1)
//||m_sWords[i][0]==0
{
//Set end for words like "张/华/平"
if (m_tagType != TAG_TYPE.TT_NORMAL)
m_nTags[i, 0] = 101;
else
m_nTags[i, 0] = 1;
m_dFrequency[i, 0] = 0;
m_sWords[i] = null; //Set virtual ending
m_nTags[i++, 1] = -1;
}
m_nCurLength = i; //The current word count
if (nRetPos != -1)
return nWordsIndex + 1;
//Next start position
return -1; //Reaching ending
}
//====================================================================
// Func Name : GenerateWordNet
// Description: Generate the segmentation word net according
// the original sentence
// Parameters : sSentence: the sentence
// dictCore : core dictionary
// bOriginalFreq=false: output original frequency
// Returns : bool
//====================================================================
public static RowFirstDynamicArray<ChainContent> GenerateWordNet(List<AtomNode> atomSegment, WordDictionary coreDict)
{
string sWord = "", sMaxMatchWord;
int nPOSRet, nPOS, nTotalFreq;
double dValue = 0;
RowFirstDynamicArray<ChainContent> m_segGraph = new RowFirstDynamicArray<ChainContent>();
m_segGraph.SetEmpty();
// 将原子部分存入m_segGraph
for (int i = 0; i < atomSegment.Count; i++)//Init the cost array
{
if (atomSegment[i].nPOS == Predefine.CT_CHINESE)
m_segGraph.SetElement(i, i + 1, new ChainContent(0, 0, atomSegment[i].sWord));
else
{
sWord = atomSegment[i].sWord;//init the word
dValue = Predefine.MAX_FREQUENCE;
switch (atomSegment[i].nPOS)
{
case Predefine.CT_INDEX:
case Predefine.CT_NUM:
nPOS = -27904;//'m'*256
sWord = "未##数";
dValue = 0;
break;
case Predefine.CT_DELIMITER:
nPOS = 30464;//'w'*256;
break;
case Predefine.CT_LETTER:
nPOS = -28280; // -'n' * 256 - 'x';
dValue = 0;
sWord = "未##串";
break;
case Predefine.CT_SINGLE://12021-2129-3121
if (Regex.IsMatch(atomSegment[i].sWord, @"^(-?\d+)(\.\d+)?$")) //匹配浮点数
{
nPOS = -27904;//'m'*256
sWord = "未##数";
}
else
{
nPOS = -28280; // -'n' * 256 - 'x'
sWord = "未##串";
}
dValue = 0;
break;
default:
nPOS = atomSegment[i].nPOS;//'?'*256;
break;
}
m_segGraph.SetElement(i, i + 1, new ChainContent(dValue, nPOS, sWord));//init the link with minimum
}
}
// 将所有可能的组词存入m_segGraph
for (int i = 0; i < atomSegment.Count; i++)//All the word
{
sWord = atomSegment[i].sWord;//Get the current atom
int j = i + 1;
while (j < atomSegment.Count && coreDict.GetMaxMatch(sWord, out sMaxMatchWord, out nPOSRet))
{
if (sMaxMatchWord == sWord) // 就是我们要找的词
{
WordInfo info = coreDict.GetWordInfo(sWord); // 该词可能就有多种词性
// 计算该词的所有词频之和
nTotalFreq = 0;
for (int k = 0; k < info.Count; k++)
nTotalFreq += info.Frequencies[k];
// 限制出现某些特殊词
if (sWord.Length == 2 && (sWord.StartsWith("年") || sWord.StartsWith("月")) && i >= 1 &&
(Utility.IsAllNum(atomSegment[i - 1].sWord) ||
Utility.IsAllChineseNum(atomSegment[i - 1].sWord)))
{
//1年内、1999年末
if ("末内中底前间初".IndexOf(sWord.Substring(1)) >= 0)
break;
}
// 如果该词只有一个词性，则存储，否则词性记录为 0
if (info.Count == 1)
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, info.POSs[0], sWord));
else
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, 0, sWord));
}
sWord += atomSegment[j++].sWord;
}
}
return m_segGraph;
}
public bool PersonRecognize(WordDictionary personDict)
{
StringBuilder sb = new StringBuilder();
int i;
string sPOS = "z", sPersonName;
string[] sPatterns = { "BBCD", "BBC", "BBE", "BBZ", "BCD", "BEE", "BE", "BG", "BXD", "BZ", "CDCD", "CD", "EE", "FB", "Y", "XD", "" };
double[] dFactor = { 0.003606, 0.000021, 0.001314, 0.000315, 0.656624, 0.000021, 0.146116, 0.009136,
0.000042, 0.038971, 0, 0.090367, 0.000273, 0.009157, 0.034324, 0.009735, 0 };
/*------------------------------------
About parameter:
BBCD 343 0.003606
BBC 2 0.000021
BBE 125 0.001314
BBZ 30 0.000315
BCD 62460 0.656624
BEE 0 0.000000
BE 13899 0.146116
BG 869 0.009136
BXD 4 0.000042
BZ 3707 0.038971
CD 8596 0.090367
EE 26 0.000273
FB 871 0.009157
Y 3265 0.034324
XD 926 0.009735
The person recognition patterns set
BBCD:姓+姓+名1+名2;
BBE: 姓+姓+单名;
BBZ: 姓+姓+双名成词;
BCD: 姓+名1+名2;
BE: 姓+单名;
BEE: 姓+单名+单名;韩磊磊
BG: 姓+后缀
BXD: 姓+姓双名首字成词+双名末字
BZ: 姓+双名成词;
B: 姓
CD: 名1+名2;
EE: 单名+单名;
FB: 前缀+姓
XD: 姓双名首字成词+双名末字
Y: 姓单名成词
------------------------------------*/
int[] nPatternLen = { 4, 3, 3, 3, 3, 3, 2, 2, 3, 2, 4, 2, 2, 2, 1, 2, 0 };
//Convert to string from POS
sb.Append('z');
for (i = 1; m_nBestTag[i] > -1; i++)
sb.Append(Convert.ToChar(m_nBestTag[i] + Convert.ToInt32('A')));
sPOS = sb.ToString();
int j = 1, k, nPos; //Find the proper pattern from the first POS
int nLittleFreqCount; //Counter for the person name role with little frequecy
bool bMatched = false;
while (j < i)
{
bMatched = false;
for (k = 0; !bMatched && nPatternLen[k] > 0; k++)
{
if (string.Compare(sPatterns[k], 0, sPOS, j, nPatternLen[k]) == 0 &&
string.Compare(m_sWords[j - 1], "·") != 0 && string.Compare(m_sWords[j + nPatternLen[k]], "·") != 0)
{
//Find the proper pattern k
if (string.Compare(sPatterns[k], "FB") == 0 && (sPOS[j + 2] == 'E' || sPOS[j + 2] == 'C' || sPOS[j + 2] == 'G'))
{
//Rule 1 for exclusion:前缀+姓+名1(名2): 规则(前缀+姓)失效；
continue;
}
/*
if((strcmp(sPatterns[k],"BEE")==0||strcmp(sPatterns[k],"EE")==0)&&strcmp(m_sWords[j+nPatternLen[k]-1],m_sWords[j+nPatternLen[k]-2])!=0)
{//Rule 2 for exclusion:姓+单名+单名:单名+单名 若EE对应的字不同，规则失效.如：韩磊磊
continue;
}
if(strcmp(sPatterns[k],"B")==0&&m_nBestTag[j+1]!=12)
{//Rule 3 for exclusion: 若姓后不是后缀，规则失效.如：江主席、刘大娘
continue;
}
*/
//Get the possible name
nPos = j; //Record the person position in the tag sequence
sPersonName = null;
nLittleFreqCount = 0; //Record the number of role with little frequency
while (nPos < j + nPatternLen[k])
{
//Get the possible person name
//
if (m_nBestTag[nPos] < 4 && personDict.GetFrequency(m_sWords[nPos], m_nBestTag[nPos]) < Predefine.LITTLE_FREQUENCY)
nLittleFreqCount++;
//The counter increase
sPersonName += m_sWords[nPos];
nPos += 1;
}
/*
if(IsAllForeign(sPersonName)&&personDict.GetFrequency(m_sWords[j],1)<LITTLE_FREQUENCY)
{//Exclusion foreign name
//Rule 2 for exclusion:若均为外国人名用字 规则(名1+名2)失效
j+=nPatternLen[k]-1;
continue;
}
*/
if (string.Compare(sPatterns[k], "CDCD") == 0)
{
//Rule for exclusion
//规则(名1+名2+名1+名2)本身是排除规则:女高音歌唱家迪里拜尔演唱
//Rule 3 for exclusion:含外国人名用字 规则适用
//否则，排除规则失效:黑妞白妞姐俩拔了头筹。
if (Utility.GetForeignCharCount(sPersonName) > 0)
j += nPatternLen[k] - 1;
continue;
}
/*
if(strcmp(sPatterns[k],"CD")==0&&IsAllForeign(sPersonName))
{//
j+=nPatternLen[k]-1;
continue;
}
if(nLittleFreqCount==nPatternLen[k]||nLittleFreqCount==3)
//马哈蒂尔;小扎耶德与他的中国阿姨胡彩玲受华黎明大使之邀，
//The all roles appear with two lower frequecy,we will ignore them
continue;
*/
m_nUnknownWords[m_nUnknownWordsCount, 0] = m_nWordPosition[j];
m_nUnknownWords[m_nUnknownWordsCount, 1] = m_nWordPosition[j + nPatternLen[k]];
m_dWordsPossibility[m_nUnknownWordsCount] = -Math.Log(dFactor[k]) + ComputePossibility(j, nPatternLen[k], personDict);
//Mutiply the factor
m_nUnknownWordsCount += 1;
j += nPatternLen[k];
bMatched = true;
}
}
if (!bMatched)
//Not matched, add j by 1
j += 1;
}
return true;
}
public bool PlaceRecognize(WordDictionary dictCore, WordDictionary placeDict)
{
int nStart = 1, nEnd = 1, i = 1, nTemp;
double dPanelty = 1.0; //Panelty value
while (m_nBestTag[i] > -1)
{
if (m_nBestTag[i] == 1)
//1 Trigger the recognition procession
{
nStart = i;
nEnd = nStart + 1;
//=========== by zhenyulu: 此处nEnd = nStart + 1;有些强迫之嫌，因此后面处理了一下
while (m_nBestTag[nEnd] == 1)
//
{
if (nEnd > nStart + 1)
{
dPanelty += 1.0;
}
nEnd++;
}
while (m_nBestTag[nEnd] == 2)
{
//2,12,22
nEnd++;
}
nTemp = nEnd;
while (m_nBestTag[nEnd] == 3)
{
if (nEnd > nTemp)
{
dPanelty += 1.0;
}
nEnd++;
}
}
else if (m_nBestTag[i] == 2)
//1,11,21 Trigger the recognition
{
dPanelty += 1.0;
nStart = i;
nEnd = nStart + 1;
while (m_nBestTag[nEnd] == 2)
{
//2
nEnd++;
}
nTemp = nEnd;
while (m_nBestTag[nEnd] == 3)
//2
{
if (nEnd > nTemp)
{
dPanelty += 1.0;
}
nEnd++;
}
}
if (nEnd > nStart)
{
//=========== by zhenyulu: 避免上面强迫之嫌带来的负面影响
if (m_sWords[nEnd] == null)
{
nEnd--;
}
m_nUnknownWords[m_nUnknownWordsCount, 0] = m_nWordPosition[nStart];
m_nUnknownWords[m_nUnknownWordsCount, 1] = m_nWordPosition[nEnd];
m_dWordsPossibility[m_nUnknownWordsCount++] = ComputePossibility(nStart, nEnd - nStart + 1, placeDict) +
Math.Log(dPanelty);
nStart = nEnd;
}
if (i < nEnd)
{
i = nEnd;
}
else
{
i = i + 1;
}
}
return(true);
}
public static void TestBiSegment()
{
List<string> sentence = new List<string>();
List<string> description = new List<string>();
sentence.Add(@"他说的的确实在理");
description.Add(@"普通分词测试");
sentence.Add(@"张华平3－4月份来北京开会");
description.Add(@"数字切分");
sentence.Add(@"1.加强管理");
description.Add(@"剔除多余的“.”");
sentence.Add(@"他出生于1980年1月1日10点");
description.Add(@"日期合并");
sentence.Add(@"他出生于甲子年");
description.Add(@"年份识别");
sentence.Add(@"馆内陈列周恩来和邓颖超生前使用过的物品");
description.Add(@"姓名识别");
WordDictionary coreDict = new WordDictionary();
if (!coreDict.Load(coreDictFile))
{
Console.WriteLine("coreDict 字典装入错误！");
return;
}
WordDictionary biDict = new WordDictionary();
if (!biDict.Load(biDictFile))
{
Console.WriteLine("字典装入错误！");
return;
}
string sSentence;
string sDescription;
for (int i = 0; i < sentence.Count; i++)
{
sSentence = sentence[i];
sDescription = description[i];
Console.WriteLine("\r\n============ {0} ============", sDescription);
sSentence = Predefine.SENTENCE_BEGIN + sSentence + Predefine.SENTENCE_END;
List<AtomNode> nodes = Segment.AtomSegment(sSentence);
Console.WriteLine("原子切分：");
for (int j = 0; j < nodes.Count; j++)
Console.Write("{0}, ", nodes[j].sWord);
Console.WriteLine("\r\n\r\n实际切分：");
Segment segment = new Segment(biDict, coreDict);
segment.BiSegment(sSentence, 0.1, 1);
for (int k = 0; k < segment.m_pWordSeg.Count; k++)
{
for (int j = 0; j < segment.m_pWordSeg[k].Length; j++)
Console.Write("{0}, ", segment.m_pWordSeg[k][j].sWord);
Console.WriteLine();
}
}
}
public static void TestGenerateWordNet()
{
WordDictionary coreDict = new WordDictionary();
if (!coreDict.Load(coreDictFile))
{
Console.WriteLine("字典装入错误！");
return;
}
string sSentence = @"人民币现在很值钱";
sSentence = Predefine.SENTENCE_BEGIN + sSentence + Predefine.SENTENCE_END;
List<AtomNode> atomSegment = Segment.AtomSegment(sSentence);
RowFirstDynamicArray<ChainContent> m_segGraph = Segment.GenerateWordNet(atomSegment, coreDict);
Console.WriteLine(m_segGraph.ToString());
}
public static void TestDictionary()
{
WordDictionary dict = new WordDictionary();
if (dict.Load(coreDictFile, false))
{
for (int j = 2; j <= 5; j++)
{
Console.WriteLine("====================================\r\n汉字:{0}, ID ：{1}\r\n", Utility.CC_ID2Char(j), j);
Console.WriteLine(" 词长 频率 词性 词");
for (int i = 0; i < dict.indexTable[j].nCount; i++)
Console.WriteLine("{0,5} {1,6} {2,5} ({3}){4}",
dict.indexTable[j].WordItems[i].nWordLen,
dict.indexTable[j].WordItems[i].nFrequency,
Utility.GetPOSString(dict.indexTable[j].WordItems[i].nPOS),
Utility.CC_ID2Char(j),
dict.indexTable[j].WordItems[i].sWord);
}
}
else
Console.WriteLine("Wrong!");
}
//====================================================================
//Merge dict2 into current dictionary and the frequency ratio from dict2 and current dict is nRatio
//====================================================================
public bool Merge(WordDictionary dict2, int nRatio)
{
int i, j, k, nCmpValue;
string sWord;
//Modification made, not to output when modify table exists.
if (modifyTable != null || dict2.modifyTable != null)
return false;
for (i = 0; i < Predefine.CC_NUM; i++)
{
j = 0;
k = 0;
while (j < indexTable[i].nCount && k < dict2.indexTable[i].nCount)
{
nCmpValue = Utility.CCStringCompare(indexTable[i].WordItems[j].sWord, dict2.indexTable[i].WordItems[k].sWord);
if (nCmpValue == 0)
//Same Words and determine the different handle
{
if (indexTable[i].WordItems[j].nPOS < dict2.indexTable[i].WordItems[k].nPOS)
nCmpValue = -1;
else if (indexTable[i].WordItems[j].nPOS > dict2.indexTable[i].WordItems[k].nPOS)
nCmpValue = 1;
}
if (nCmpValue == 0)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency + dict2.indexTable[i].WordItems[k].nFrequency) / (nRatio + 1);
j += 1;
k += 1;
}
//Get next word in the current dictionary
else if (nCmpValue < 0)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency) / (nRatio + 1);
j += 1;
}
else
//Get next word in the second dictionary
{
if (dict2.indexTable[i].WordItems[k].nFrequency > (nRatio + 1) / 10)
{
sWord = string.Format("{0}{1}", Utility.CC_ID2Char(i).ToString(), dict2.indexTable[i].WordItems[k].sWord);
AddItem(sWord, dict2.indexTable[i].WordItems[k].nPOS, dict2.indexTable[i].WordItems[k].nFrequency / (nRatio + 1));
}
k += 1;
}
}
//words in current dictionary are left
while (j < indexTable[i].nCount)
{
indexTable[i].WordItems[j].nFrequency = (nRatio * indexTable[i].WordItems[j].nFrequency) / (nRatio + 1);
j += 1;
}
//words in Dict2 are left
while (k < dict2.indexTable[i].nCount)
{
if (dict2.indexTable[i].WordItems[k].nFrequency > (nRatio + 1) / 10)
{
sWord = string.Format("{0}{1}", Utility.CC_ID2Char(i).ToString(), dict2.indexTable[i].WordItems[k].sWord);
AddItem(sWord, dict2.indexTable[i].WordItems[k].nPOS, dict2.indexTable[i].WordItems[k].nFrequency / (nRatio + 1));
}
k += 1;
}
}
return true;
}
//====================================================================
// Func Name : GenerateWordNet
// Description: Generate the segmentation word net according
// the original sentence
// Parameters : sSentence: the sentence
// dictCore : core dictionary
// bOriginalFreq=false: output original frequency
// Returns : bool
//====================================================================
public static RowFirstDynamicArray<ChainContent> GenerateWordNet(List<AtomNode> atomSegment, WordDictionary coreDict)
{
string sWord = "", sMaxMatchWord;
int nPOSRet, nPOS, nTotalFreq;
double dValue = 0;
RowFirstDynamicArray<ChainContent> m_segGraph = new RowFirstDynamicArray<ChainContent>();
m_segGraph.SetEmpty();
// ��ԭ�Ӳ��ִ���m_segGraph
for (int i = 0; i < atomSegment.Count; i++)//Init the cost array
{
if (atomSegment[i].nPOS == Predefine.CT_CHINESE)
m_segGraph.SetElement(i, i + 1, new ChainContent(0, 0, atomSegment[i].sWord));
else
{
sWord = atomSegment[i].sWord;//init the word
dValue = Predefine.MAX_FREQUENCE;
switch (atomSegment[i].nPOS)
{
case Predefine.CT_INDEX:
case Predefine.CT_NUM:
nPOS = -27904;//'m'*256
sWord = "δ##��";
dValue = 0;
break;
case Predefine.CT_DELIMITER:
nPOS = 30464;//'w'*256;
break;
case Predefine.CT_LETTER:
nPOS = -28280; // -'n' * 256 - 'x';
dValue = 0;
sWord = "δ##��";
break;
case Predefine.CT_SINGLE://12021-2129-3121
if (Regex.IsMatch(atomSegment[i].sWord, @"^(-?\d+)(\.\d+)?$"))����//ƥ�両����
{
nPOS = -27904;//'m'*256
sWord = "δ##��";
}
else
{
nPOS = -28280; // -'n' * 256 - 'x'
sWord = "δ##��";
}
dValue = 0;
break;
default:
nPOS = atomSegment[i].nPOS;//'?'*256;
break;
}
m_segGraph.SetElement(i, i + 1, new ChainContent(dValue, nPOS, sWord));//init the link with minimum
}
}
// �����п��ܵ���ʴ���m_segGraph
for (int i = 0; i < atomSegment.Count; i++)//All the word
{
sWord = atomSegment[i].sWord;//Get the current atom
int j = i + 1;
while (j < atomSegment.Count && coreDict.GetMaxMatch(sWord, out sMaxMatchWord, out nPOSRet))
{
if (sMaxMatchWord == sWord) // ��������Ҫ�ҵĴ�
{
WordInfo info = coreDict.GetWordInfo(sWord); // �ôʿ��ܾ��ж��ִ���
// ����ôʵ����д�Ƶ֮��
nTotalFreq = 0;
for (int k = 0; k < info.Count; k++)
nTotalFreq += info.Frequencies[k];
// ���Ƴ���ijЩ�����
if (sWord.Length == 2 && (sWord.StartsWith("��") || sWord.StartsWith("��")) && i >= 1 &&
(Utility.IsAllNum(atomSegment[i - 1].sWord) ||
Utility.IsAllChineseNum(atomSegment[i - 1].sWord)))
{
//1���ڡ�1999��ĩ
if ("ĩ���е�ǰ���".IndexOf(sWord.Substring(1)) >= 0)
break;
}
// ����ô�ֻ��һ�����ԣ���洢��������Լ�¼Ϊ 0
if (info.Count == 1)
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, info.POSs[0], sWord));
else
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, 0, sWord));
}
sWord += atomSegment[j++].sWord;
}
}
return m_segGraph;
}
//====================================================================
// Func Name : GenerateWordNet
// Description: Generate the segmentation word net according
// the original sentence
// Parameters : sSentence: the sentence
// dictCore : core dictionary
// bOriginalFreq=false: output original frequency
// Returns : bool
//====================================================================
public static RowFirstDynamicArray <ChainContent> GenerateWordNet(List <AtomNode> atomSegment, WordDictionary coreDict)
{
string sWord = "", sMaxMatchWord;
int nPOSRet, nPOS, nTotalFreq;
double dValue = 0;
RowFirstDynamicArray <ChainContent> m_segGraph = new RowFirstDynamicArray <ChainContent>();
m_segGraph.SetEmpty();
// 将原子部分存入m_segGraph
for (int i = 0; i < atomSegment.Count; i++)//Init the cost array
{
if (atomSegment[i].nPOS == Predefine.CT_CHINESE)
{
m_segGraph.SetElement(i, i + 1, new ChainContent(0, 0, atomSegment[i].sWord));
}
else
{
sWord = atomSegment[i].sWord;//init the word
dValue = Predefine.MAX_FREQUENCE;
switch (atomSegment[i].nPOS)
{
case Predefine.CT_INDEX:
case Predefine.CT_NUM:
nPOS = -27904;//'m'*256
sWord = "未##数";
dValue = 0;
break;
case Predefine.CT_DELIMITER:
nPOS = 30464;//'w'*256;
break;
case Predefine.CT_LETTER:
nPOS = -28280; // -'n' * 256 - 'x';
dValue = 0;
sWord = "未##串";
break;
case Predefine.CT_SINGLE://12021-2129-3121
if (Regex.IsMatch(atomSegment[i].sWord, @"^(-?\d+)(\.\d+)?$"))
{
//匹配浮点数
{
nPOS = -27904; //'m'*256
sWord = "未##数";
}
}
else
{
nPOS = -28280; // -'n' * 256 - 'x'
sWord = "未##串";
}
dValue = 0;
break;
default:
nPOS = atomSegment[i].nPOS;//'?'*256;
break;
}
m_segGraph.SetElement(i, i + 1, new ChainContent(dValue, nPOS, sWord));//init the link with minimum
}
}
// 将所有可能的组词存入m_segGraph
for (int i = 0; i < atomSegment.Count; i++) //All the word
{
sWord = atomSegment[i].sWord; //Get the current atom
int j = i + 1;
while (j < atomSegment.Count && coreDict.GetMaxMatch(sWord, out sMaxMatchWord, out nPOSRet))
{
if (sMaxMatchWord == sWord) // 就是我们要找的词
{
WordInfo info = coreDict.GetWordInfo(sWord); // 该词可能就有多种词性
// 计算该词的所有词频之和
nTotalFreq = 0;
for (int k = 0; k < info.Count; k++)
{
nTotalFreq += info.Frequencies[k];
}
// 限制出现某些特殊词
if (sWord.Length == 2 && (sWord.StartsWith("年") || sWord.StartsWith("月")) && i >= 1 &&
(Utility.IsAllNum(atomSegment[i - 1].sWord) ||
Utility.IsAllChineseNum(atomSegment[i - 1].sWord)))
{
//1年内、1999年末
if ("末内中底前间初".IndexOf(sWord.Substring(1)) >= 0)
{
break;
}
}
// 如果该词只有一个词性，则存储，否则词性记录为 0
if (info.Count == 1)
{
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, info.POSs[0], sWord));
}
else
{
m_segGraph.SetElement(i, j, new ChainContent(nTotalFreq, 0, sWord));
}
}
sWord += atomSegment[j++].sWord;
}
}
return(m_segGraph);
}
public Segment(WordDictionary biDict, WordDictionary coreDict)
{
this.biDict = biDict;
this.coreDict = coreDict;
}
//====================================================================
// 生成两两词之间的二叉图表
//====================================================================
public static ColumnFirstDynamicArray <ChainContent> BiGraphGenerate(
RowFirstDynamicArray <ChainContent> aWord, double smoothPara, WordDictionary biDict, WordDictionary coreDict)
{
ColumnFirstDynamicArray <ChainContent> aBiWordNet = new ColumnFirstDynamicArray <ChainContent>();
ChainItem <ChainContent> pCur, pNextWords;
int nTwoWordsFreq = 0, nCurWordIndex, nNextWordIndex;
double dCurFreqency, dValue, dTemp;
string sTwoWords;
StringBuilder sb = new StringBuilder();
//Record the position map of possible words
int[] m_npWordPosMapTable = PreparePositionMap(aWord);
pCur = aWord.GetHead();
while (pCur != null)
{
if (pCur.Content.nPOS >= 0)
{
//It's not an unknown words
dCurFreqency = pCur.Content.eWeight;
}
else
{
//Unknown words
dCurFreqency = coreDict.GetFrequency(pCur.Content.sWord, 2);
}
//Get next words which begin with pCur.col（注：很特殊的对应关系）
pNextWords = aWord.GetFirstElementOfRow(pCur.col);
while (pNextWords != null && pNextWords.row == pCur.col)
{
sb.Remove(0, sb.Length);
sb.Append(pCur.Content.sWord);
sb.Append(Predefine.WORD_SEGMENTER);
sb.Append(pNextWords.Content.sWord);
sTwoWords = sb.ToString();
//Two linked Words frequency
nTwoWordsFreq = biDict.GetFrequency(sTwoWords, 3);
//Smoothing
dTemp = 1.0 / Predefine.MAX_FREQUENCE;
//-log{a*P(Ci-1)+(1-a)P(Ci|Ci-1)} Note 0<a<1
dValue = -Math.Log(smoothPara * (1.0 + dCurFreqency) / (Predefine.MAX_FREQUENCE + 80000.0)
+ (1.0 - smoothPara) * ((1.0 - dTemp) * nTwoWordsFreq / (1.0 + dCurFreqency) +
dTemp));
//Unknown words: P(Wi|Ci);while known words:1
if (pCur.Content.nPOS < 0)
{
dValue += pCur.Content.nPOS;
}
//Get the position index of current word in the position map table
nCurWordIndex = Utility.BinarySearch(pCur.row * Predefine.MAX_SENTENCE_LEN + pCur.col, m_npWordPosMapTable);
nNextWordIndex = Utility.BinarySearch(pNextWords.row * Predefine.MAX_SENTENCE_LEN + pNextWords.col, m_npWordPosMapTable);
aBiWordNet.SetElement(nCurWordIndex, nNextWordIndex, new ChainContent(dValue, pCur.Content.nPOS, sTwoWords));
pNextWords = pNextWords.next; //Get next word
}
pCur = pCur.next;
}
return(aBiWordNet);
}
private double ComputePossibility(int nStartPos, int nLength, WordDictionary dict)
{
double dRetValue = 0, dPOSPoss;
//dPOSPoss: the possibility of a POS appears
//dContextPoss: The possibility of context POS appears
int nFreq;
for (int i = nStartPos; i < nStartPos + nLength; i++)
{
nFreq = dict.GetFrequency(m_sWords[i], m_nBestTag[i]);
//nFreq is word being the POS
dPOSPoss = Math.Log((double)(m_context.GetFrequency(0, m_nBestTag[i]) + 1)) - Math.Log((double)(nFreq + 1));
dRetValue += dPOSPoss;
/*
if(i<nStartPos+nLength-1)
{
dContextPoss=log((double)(m_context.GetContextPossibility(0,m_nBestTag[i],m_nBestTag[i+1])+1));
dRetValue+=dPOSPoss-dContextPoss;
}
*/
}
return dRetValue;
}
public Segment(WordDictionary biDict, WordDictionary coreDict)
{
this.biDict = biDict;
this.coreDict = coreDict;
}
//POS tagging with Hidden Markov Model
public bool POSTagging(WordResult[] pWordItems, WordDictionary dictCore, WordDictionary dictUnknown)
{
//pWordItems: Items; nItemCount: the count of items;core dictionary and unknown recognition dictionary
int i = 0, j, nStartPos;
Reset(false);
while (i > -1 && i < pWordItems.Length && pWordItems[i].sWord != null)
{
nStartPos = i; //Start Position
i = GetFrom(pWordItems, nStartPos, dictCore, dictUnknown);
GetBestPOS();
switch (m_tagType)
{
case TAG_TYPE.TT_NORMAL:
//normal POS tagging
j = 1;
while (m_nBestTag[j] != -1 && j < m_nCurLength)
{
//Store the best POS tagging
pWordItems[j + nStartPos - 1].nPOS = m_nBestTag[j];
//Let 。be 0
if (pWordItems[j + nStartPos - 1].dValue > 0 && dictCore.IsExist(pWordItems[j + nStartPos - 1].sWord, -1))
//Exist and update its frequncy as a POS value
pWordItems[j + nStartPos - 1].dValue = dictCore.GetFrequency(pWordItems[j + nStartPos - 1].sWord, m_nBestTag[j]);
j += 1;
}
break;
case TAG_TYPE.TT_PERSON:
//Person recognition
PersonRecognize(dictUnknown);
break;
case TAG_TYPE.TT_PLACE:
//Place name recognition
case TAG_TYPE.TT_TRANS_PERSON:
//Transliteration Person
PlaceRecognize(dictCore, dictUnknown);
break;
default:
break;
}
Reset();
}
return true;
}
public bool PersonRecognize(WordDictionary personDict)
{
StringBuilder sb = new StringBuilder();
int i;
string sPOS = "z", sPersonName;
string[] sPatterns = { "BBCD", "BBC", "BBE", "BBZ", "BCD", "BEE", "BE", "BG", "BXD", "BZ", "CDCD", "CD", "EE", "FB", "Y", "XD", "" };
double[] dFactor = { 0.003606, 0.000021, 0.001314, 0.000315, 0.656624, 0.000021, 0.146116, 0.009136,
0.000042, 0.038971, 0, 0.090367, 0.000273, 0.009157, 0.034324, 0.009735, 0 };
/*------------------------------------
* About parameter:
*
* BBCD 343 0.003606
* BBC 2 0.000021
* BBE 125 0.001314
* BBZ 30 0.000315
* BCD 62460 0.656624
* BEE 0 0.000000
* BE 13899 0.146116
* BG 869 0.009136
* BXD 4 0.000042
* BZ 3707 0.038971
* CD 8596 0.090367
* EE 26 0.000273
* FB 871 0.009157
* Y 3265 0.034324
* XD 926 0.009735
*
* The person recognition patterns set
* BBCD:姓+姓+名1+名2;
* BBE: 姓+姓+单名;
* BBZ: 姓+姓+双名成词;
* BCD: 姓+名1+名2;
* BE: 姓+单名;
* BEE: 姓+单名+单名;韩磊磊
* BG: 姓+后缀
* BXD: 姓+姓双名首字成词+双名末字
* BZ: 姓+双名成词;
* B: 姓
* CD: 名1+名2;
* EE: 单名+单名;
* FB: 前缀+姓
* XD: 姓双名首字成词+双名末字
* Y: 姓单名成词
* ------------------------------------*/
int[] nPatternLen = { 4, 3, 3, 3, 3, 3, 2, 2, 3, 2, 4, 2, 2, 2, 1, 2, 0 };
//Convert to string from POS
sb.Append('z');
for (i = 1; m_nBestTag[i] > -1; i++)
{
sb.Append(Convert.ToChar(m_nBestTag[i] + Convert.ToInt32('A')));
}
sPOS = sb.ToString();
int j = 1, k, nPos; //Find the proper pattern from the first POS
int nLittleFreqCount; //Counter for the person name role with little frequecy
bool bMatched = false;
while (j < i)
{
bMatched = false;
for (k = 0; !bMatched && nPatternLen[k] > 0; k++)
{
if (string.Compare(sPatterns[k], 0, sPOS, j, nPatternLen[k]) == 0 &&
string.Compare(m_sWords[j - 1], "·") != 0 && string.Compare(m_sWords[j + nPatternLen[k]], "·") != 0)
{
//Find the proper pattern k
if (string.Compare(sPatterns[k], "FB") == 0 && (sPOS[j + 2] == 'E' || sPOS[j + 2] == 'C' || sPOS[j + 2] == 'G'))
{
//Rule 1 for exclusion:前缀+姓+名1(名2): 规则(前缀+姓)失效；
continue;
}
/*
* if((strcmp(sPatterns[k],"BEE")==0||strcmp(sPatterns[k],"EE")==0)&&strcmp(m_sWords[j+nPatternLen[k]-1],m_sWords[j+nPatternLen[k]-2])!=0)
* {//Rule 2 for exclusion:姓+单名+单名:单名+单名 若EE对应的字不同，规则失效.如：韩磊磊
* continue;
* }
*
* if(strcmp(sPatterns[k],"B")==0&&m_nBestTag[j+1]!=12)
* {//Rule 3 for exclusion: 若姓后不是后缀，规则失效.如：江主席、刘大娘
* continue;
* }
*/
//Get the possible name
nPos = j; //Record the person position in the tag sequence
sPersonName = null;
nLittleFreqCount = 0; //Record the number of role with little frequency
while (nPos < j + nPatternLen[k])
{
//Get the possible person name
//
if (m_nBestTag[nPos] < 4 && personDict.GetFrequency(m_sWords[nPos], m_nBestTag[nPos]) < Predefine.LITTLE_FREQUENCY)
{
nLittleFreqCount++;
}
//The counter increase
sPersonName += m_sWords[nPos];
nPos += 1;
}
/*
* if(IsAllForeign(sPersonName)&&personDict.GetFrequency(m_sWords[j],1)<LITTLE_FREQUENCY)
* {//Exclusion foreign name
* //Rule 2 for exclusion:若均为外国人名用字 规则(名1+名2)失效
* j+=nPatternLen[k]-1;
* continue;
* }
*/
if (string.Compare(sPatterns[k], "CDCD") == 0)
{
//Rule for exclusion
//规则(名1+名2+名1+名2)本身是排除规则:女高音歌唱家迪里拜尔演唱
//Rule 3 for exclusion:含外国人名用字 规则适用
//否则，排除规则失效:黑妞白妞姐俩拔了头筹。
if (Utility.GetForeignCharCount(sPersonName) > 0)
{
j += nPatternLen[k] - 1;
}
continue;
}
/*
* if(strcmp(sPatterns[k],"CD")==0&&IsAllForeign(sPersonName))
* {//
* j+=nPatternLen[k]-1;
* continue;
* }
* if(nLittleFreqCount==nPatternLen[k]||nLittleFreqCount==3)
* //马哈蒂尔;小扎耶德与他的中国阿姨胡彩玲受华黎明大使之邀，
* //The all roles appear with two lower frequecy,we will ignore them
* continue;
*/
m_nUnknownWords[m_nUnknownWordsCount, 0] = m_nWordPosition[j];
m_nUnknownWords[m_nUnknownWordsCount, 1] = m_nWordPosition[j + nPatternLen[k]];
m_dWordsPossibility[m_nUnknownWordsCount] = -Math.Log(dFactor[k]) + ComputePossibility(j, nPatternLen[k], personDict);
//Mutiply the factor
m_nUnknownWordsCount += 1;
j += nPatternLen[k];
bMatched = true;
}
}
if (!bMatched)
{
//Not matched, add j by 1
j += 1;
}
}
return(true);
}
/// <summary>
/// 得到所有可能的分词方案
/// </summary>
/// <returns></returns>
public RowFirstDynamicArray<ChainContent> GetSegGraph(string sSentence)
{
WordDictionary coreDict = new WordDictionary();
if (!coreDict.Load(coreDictFile))
{
Console.WriteLine("字典装入错误！");
return null;
}
//string sSentence = @"他说的确实实在";
sSentence = Predefine.SENTENCE_BEGIN + sSentence + Predefine.SENTENCE_END;
List<AtomNode> atomSegment = Segment.AtomSegment(sSentence);
RowFirstDynamicArray<ChainContent> m_segGraph = Segment.GenerateWordNet(atomSegment, coreDict);
return m_segGraph;
}
/// <summary>
/// 找到导入库和现有库的不同
/// </summary>
/// <param name="NewDicFile">导入库文件</param>
/// <param name="Encoding">导入库文件编码</param>
/// <param name="DicFormat">导入库文件格式</param>
/// <param name="SourceDict">原库对象</param>
/// <param name="OddLines">输出没有词性标注且现有库中也没有的词行</param>
/// <param name="NewWords">输出新词或现有词的新词性</param>
/// <param name="ExistWords">输出重复词，且词性也相同</param>
/// <param name="MaxFrqRate">重复词的最大词频比例</param>
/// <param name="MinFrqRate">重复词的最小词频比例</param>
/// <param name="AvgFrqRate">重复词的平均词频比例</param>
public static void FindDifferent(string NewDicFile, Encoding Encoding, DictionaryFormat DicFormat, WordDictionary SourceDict,
out string[] OddLines, out WordDictionary.DicWordInfo[] NewWords, out WordDictionary.DicWordInfo[] ExistWords,
out double MaxFrqRate, out double MinFrqRate, out double AvgFrqRate)
{
//初始化
MaxFrqRate = double.MinValue; MinFrqRate = double.MaxValue; decimal SumFrqRate = 0;
//const string[] CheckPos = new string[] { "n", "ns", "nr", "ng", "v", "j", "m", "vn", "a", "q" };
//准备词性转换
Dictionary<string, string> PosTrans = getPosTransformMap(DicFormat);
//加载词库
Dictionary<string, WordDictionary.DicWordInfo> OldWords = SourceDict.ToWordDictionary(); ;
//内存词组
List<string> Odds = new List<string>(OldWords.Count / 2);
List<WordDictionary.DicWordInfo> Exists = new List<SharpICTCLAS.WordDictionary.DicWordInfo>(OldWords.Count / 2);
List<WordDictionary.DicWordInfo> News = new List<WordDictionary.DicWordInfo>(OldWords.Count / 2);
//加载词库并统计库内有的词的词频，以估算词频转换的比例关系
foreach (string Line in File.ReadAllLines(NewDicFile, Encoding))
{
string Word;
int Frq;
string Poses;
switch (DicFormat)
{
case DictionaryFormat.SogouW2006:
string[] s = Line.Split('\t', ' ');
Word = s[0];
Frq = s.Length == 1 ? -1 : int.Parse(s[1]);
Poses = s.Length < 2 ? null : s[2];
break;
case DictionaryFormat.ExcelCSV:
default:
int p1 = Line.IndexOf(',');
int p2 = Line.IndexOf(',', p1 + 1);
Word = Line.Substring(0, p1);
Frq = int.Parse(Line.Substring(p1 + 1, p2 - p1 - 1));
Poses = Line.Substring(p2 + 1).Trim('"').Trim();
break;
}
if (string.IsNullOrEmpty(Poses))
{
if (!OldWords.ContainsKey(Word.ToLower())) Odds.Add(Line);
continue;
}
foreach (string InputPos in Poses.TrimEnd(',').Split(','))
{
if (string.IsNullOrEmpty(InputPos)) continue;
//如果映射表中没有，则保留原始词性字母
string Pos = PosTrans.ContainsKey(InputPos.ToLower()) ? PosTrans[InputPos.ToLower()] : InputPos.ToLower();
//是否存在
if (OldWords.ContainsKey(Word.ToLower()) && OldWords[Word.ToLower()].Pos.Contains(Pos))
{
int SourceFrq = OldWords[Word.ToLower()].Frequence;
double FrqR = SourceFrq == 0 ? Frq : (double)Frq / SourceFrq;
if (FrqR > MaxFrqRate) MaxFrqRate = FrqR;
if (FrqR < MinFrqRate) MinFrqRate = FrqR;
SumFrqRate += (decimal)FrqR;
Exists.Add(new WordDictionary.DicWordInfo(Word, Pos, Frq));
}
else //新词或新词性
{
News.Add(new WordDictionary.DicWordInfo(Word, Pos, Frq));
}
}
}
//平均频度转换倍数
AvgFrqRate = Exists.Count > 0 ? Convert.ToDouble(SumFrqRate / Exists.Count) : 0;
OddLines = Odds.ToArray();
NewWords = News.ToArray();
ExistWords = Exists.ToArray();
}
/// <summary>
/// 找到导入库和现有库的不同
/// </summary>
/// <param name="NewDicFile">导入库文件</param>
/// <param name="Encoding">导入库文件编码</param>
/// <param name="DicFormat">导入库文件格式</param>
/// <param name="SourceDictFileName">原库文件</param>
/// <param name="OddLines">输出没有词性标注且现有库中也没有的词行</param>
/// <param name="NewWords">输出新词或现有词的新词性</param>
/// <param name="ExistWords">输出重复词，且词性也相同</param>
/// <param name="MaxFrqRate">重复词的最大词频比例</param>
/// <param name="MinFrqRate">重复词的最小词频比例</param>
/// <param name="AvgFrqRate">重复词的平均词频比例</param>
public static void FindDifferent(string NewDicFile, Encoding Encoding, DictionaryFormat DicFormat, string SourceDictFileName,
out string[] OddLines, out WordDictionary.DicWordInfo[] NewWords, out WordDictionary.DicWordInfo[] ExistWords,
out double MaxFrqRate, out double MinFrqRate, out double AvgFrqRate)
{
WordDictionary SourceDict = new WordDictionary();
if (!SourceDict.Load(SourceDictFileName))
throw new Exception("load source dic file fail");
FindDifferent(NewDicFile, Encoding, DicFormat, SourceDict, out OddLines, out NewWords, out ExistWords, out MaxFrqRate, out MinFrqRate, out AvgFrqRate);
SourceDict.ReleaseDict();
}
private int GetFrom(WordResult[] pWordItems, int nIndex, WordDictionary dictCore, WordDictionary dictUnknown)
{
WordInfo info;
int[] aPOS = new int[Predefine.MAX_POS_PER_WORD];
int[] aFreq = new int[Predefine.MAX_POS_PER_WORD];
int nFreq = 0, j, nRetPos = 0, nWordsIndex = 0;
bool bSplit = false; //Need to split in Transliteration recognition
int i = 1, nPOSCount;
string sCurWord; //Current word
nWordsIndex = i + nIndex - 1;
for (i = 1; i < Predefine.MAX_WORDS_PER_SENTENCE && nWordsIndex < pWordItems.Length; i++)
{
if (m_tagType == TAG_TYPE.TT_NORMAL || !dictUnknown.IsExist(pWordItems[nWordsIndex].sWord, 44))
{
m_sWords[i] = pWordItems[nWordsIndex].sWord; //store current word
m_nWordPosition[i + 1] = m_nWordPosition[i] + m_sWords[i].Length;
}
else
{
if (!bSplit)
{
m_sWords[i] = pWordItems[nWordsIndex].sWord.Substring(0, 1);
//store current word
bSplit = true;
}
else
{
m_sWords[i] = pWordItems[nWordsIndex].sWord.Substring(1);
//store current word
bSplit = false;
}
m_nWordPosition[i + 1] = m_nWordPosition[i] + m_sWords[i].Length;
}
//Record the position of current word
m_nStartPos = m_nWordPosition[i + 1];
//Move the Start POS to the ending
if (m_tagType != TAG_TYPE.TT_NORMAL)
{
//Get the POSs from the unknown recognition dictionary
sCurWord = m_sWords[i];
if (m_tagType == TAG_TYPE.TT_TRANS_PERSON && i > 0 && m_sWords[i - 1] != null &&
Utility.charType(m_sWords[i - 1].ToCharArray()[0]) == Predefine.CT_CHINESE)
{
if (m_sWords[i] == ".")
{
sCurWord = "．";
}
else if (m_sWords[i] == "-")
{
sCurWord = "－";
}
}
info = dictUnknown.GetWordInfo(sCurWord);
if (info != null)
{
nPOSCount = info.Count + 1;
for (j = 0; j < info.Count; j++)
{
//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i, j] = info.POSs[j];
m_dFrequency[i, j] = -Math.Log((double)(1 + info.Frequencies[j])) +
Math.Log((double)(m_context.GetFrequency(0, info.POSs[j]) + nPOSCount));
}
}
else
{
nPOSCount = 1;
j = 0;
}
//Get the POS set of sCurWord in the core dictionary
//We ignore the POS in the core dictionary and recognize them as other (0).
//We add their frequency to get the possibility as POS 0
if (string.Compare(m_sWords[i], "始##始") == 0)
{
m_nTags[i, j] = 100;
m_dFrequency[i, j] = 0;
j++;
}
else if (string.Compare(m_sWords[i], "末##末") == 0)
{
m_nTags[i, j] = 101;
m_dFrequency[i, j] = 0;
j++;
}
else
{
//dictCore.GetHandle(m_sWords[i], &nCount, aPOS, aFreq);
info = dictCore.GetWordInfo(m_sWords[i]);
nFreq = 0;
if (info != null)
{
for (int k = 0; k < info.Count; k++)
{
nFreq += info.Frequencies[k];
}
if (info.Count > 0)
{
m_nTags[i, j] = 0;
//m_dFrequency[i][j]=(double)(1+nFreq)/(double)(m_context.GetFrequency(0,0)+1);
m_dFrequency[i, j] = -Math.Log((double)(1 + nFreq)) + Math.Log((double)(m_context.GetFrequency(0, 0) + nPOSCount));
j++;
}
}
}
}
else
//For normal POS tagging
{
j = 0;
//Get the POSs from the unknown recognition dictionary
if (pWordItems[nWordsIndex].nPOS > 0)
{
//The word has is only one POS value
//We have record its POS and nFrequncy in the items.
m_nTags[i, j] = pWordItems[nWordsIndex].nPOS;
m_dFrequency[i, j] = -Math.Log(pWordItems[nWordsIndex].dValue) + Math.Log((double)(m_context.GetFrequency(0, m_nTags[i, j]) + 1));
if (m_dFrequency[i, j] < 0)
{
//Not permit the value less than 0
m_dFrequency[i, j] = 0;
}
j++;
}
else
{
//The word has multiple POSs, we should retrieve the information from Core Dictionary
if (pWordItems[nWordsIndex].nPOS < 0)
{
//The word has is only one POS value
//We have record its POS and nFrequncy in the items.
m_nTags[i, j] = -pWordItems[nWordsIndex].nPOS;
m_dFrequency[i, j++] = pWordItems[nWordsIndex].dValue;
}
//dictCore.GetHandle(m_sWords[i], &nCount, aPOS, aFreq);
info = dictCore.GetWordInfo(m_sWords[i]);
if (info != null)
{
nPOSCount = info.Count;
for (; j < info.Count; j++)
{
//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i, j] = info.POSs[j];
m_dFrequency[i, j] = -Math.Log(1 + info.Frequencies[j]) + Math.Log(m_context.GetFrequency(0, m_nTags[i, j]) + nPOSCount);
}
}
}
}
if (j == 0)
{
//We donot know the POS, so we have to guess them according lexical knowledge
GuessPOS(i, out j); //Guess the POS of current word
}
m_nTags[i, j] = -1; //Set the ending POS
if (j == 1 && m_nTags[i, j] != Predefine.CT_SENTENCE_BEGIN)
//No ambuguity
{
//No ambuguity, so we can break from the loop
i++;
m_sWords[i] = null;
break;
}
if (!bSplit)
{
nWordsIndex++;
}
}
if (nWordsIndex == pWordItems.Length)
{
nRetPos = -1;
}
//Reaching ending
if (m_nTags[i - 1, 1] != -1)
//||m_sWords[i][0]==0
{
//Set end for words like "张/华/平"
if (m_tagType != TAG_TYPE.TT_NORMAL)
{
m_nTags[i, 0] = 101;
}
else
{
m_nTags[i, 0] = 1;
}
m_dFrequency[i, 0] = 0;
m_sWords[i] = null; //Set virtual ending
m_nTags[i++, 1] = -1;
}
m_nCurLength = i; //The current word count
if (nRetPos != -1)
{
return(nWordsIndex + 1);
}
//Next start position
return(-1); //Reaching ending
}
