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();
// ��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;
}
//====================================================================
// 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);
}
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
}