bool CResult::ChineseNameSplit(char *sPersonName, char *sSurname, char *sSurname2, char *sGivenName, CDictionary &personDict)
{
int nSurNameLen=4,nLen=strlen(sPersonName),nFreq,i=0,nCharType,nFreqGiven;
char sTemp[3];
if(nLen<3||nLen>8)//Not a traditional Chinese person name
return false;
while(i<nLen)//No Including non-CHinese char
{
nCharType=charType((unsigned char*)sPersonName+i);
if(nCharType!=CT_CHINESE&&nCharType!=CT_OTHER)
return false;
i+=2;
}
sSurname2[0]=0;//init
strncpy(sSurname,sPersonName,nSurNameLen);
sSurname[nSurNameLen]=0;
if(!personDict.IsExist(sSurname,1))
{
nSurNameLen=2;
sSurname[nSurNameLen]=0;
if(!personDict.IsExist(sSurname,1))
{
nSurNameLen=0;
sSurname[nSurNameLen]=0;
}
}
strcpy(sGivenName,sPersonName+nSurNameLen);
if(nLen>6)
{
strncpy(sTemp,sPersonName+nSurNameLen,2);
sTemp[2]=0;//Get the second possible surname
if(personDict.IsExist(sTemp,1))
{//Hongkong women's name: Surname+surname+given name
strcpy(sSurname2,sTemp);
strcpy(sGivenName,sPersonName+nSurNameLen+2);
}
}
nFreq=personDict.GetFrequency(sSurname,1);
strncpy(sTemp,sGivenName,2);
sTemp[2]=0;
nFreqGiven=personDict.GetFrequency(sTemp,2);
if(nSurNameLen!=4&&((nSurNameLen==0&&nLen>4)||strlen(sGivenName)>4||(GetForeignCharCount(sPersonName)>=3&&nFreq<personDict.GetFrequency("张",1)/40&&nFreqGiven<personDict.GetFrequency("华",2)/20)||(nFreq<10&&GetForeignCharCount(sGivenName)==(nLen-nSurNameLen)/2)))
return false;
if(nLen==4&&m_uPerson.IsGivenName(sPersonName))
{//Single Surname+given name
return false;
}
return true;
}
//POS tagging with Hidden Markov Model
bool CSpan::POSTagging(PWORD_RESULT pWordItems,CDictionary &dictCore,CDictionary &dictUnknown)
{
//pWordItems: Items; nItemCount: the count of items;core dictionary and unknown recognition dictionary
int i=0,j,nStartPos;
Reset(false);
while(i>-1&&pWordItems[i].sWord[0]!=0)
{
nStartPos=i;//Start Position
i=GetFrom(pWordItems,nStartPos,dictCore,dictUnknown);
GetBestPOS();
switch(m_tagType)
{
case TT_NORMAL://normal POS tagging
j=1;
while(m_nBestTag[j]!=-1&&j<m_nCurLength)
{//Store the best POS tagging
pWordItems[j+nStartPos-1].nHandle=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=LOG_MAX_FRQUENCE-log((double)dictCore.GetFrequency(pWordItems[j+nStartPos-1].sWord,m_nBestTag[j])+1);
j+=1;
}
break;
case TT_PERSON://Person recognition
/*clock_t lStart,lEnd;
lStart=clock();
*/
SplitPersonPOS(dictUnknown);
//lEnd=clock();
//printf("SplitPersonPOS=%f\n",(double)(lEnd-lStart)*1000/CLOCKS_PER_SEC);
//Spit Persons POS
//lStart=clock();
PersonRecognize(dictUnknown);
//lEnd=clock();
//printf("PersonRecognize=%f\n",(double)(lEnd-lStart)/CLOCKS_PER_SEC);
//Person Recognition with the person recognition dictionary
break;
case TT_PLACE://Place name recognition
PlaceRecognize(dictCore,dictUnknown);
break;
case TT_TRANS://Transliteration
TransRecognize(dictCore,dictUnknown);
break;
default:
break;
}
Reset();
}
return true;
}
bool CSpan::SplitPersonPOS(CDictionary &unlistDict)
{//Split the word with POS 21 and 22
int i=m_nCurLength-1,j;
unsigned int nLenWord,nLenPart;
char sFirstPart[50],sLastPart[50];
int nFirstPOS,nLastPOS;
for(;i>0;i--)
{
if(m_nBestTag[i]==21||m_nBestTag[i]==22)
{//Find the POS which need to split
for(j=m_nCurLength-1;j>i;j--)
{//Move the POS and words
strcpy(m_sWords[j+1],m_sWords[j]);
m_nBestTag[j+1]=m_nBestTag[j];
m_nWordPosition[j+1]=m_nWordPosition[j];
}
m_nCurLength+=1;//The length increment
//Generate new segment words and POS
if(m_nBestTag[i]==21)
{//Combination by Previous and first component
nLenWord=strlen(m_sWords[i]);
if(nLenWord>4)//Get first component
{
strcpy(sLastPart,m_sWords[i]+nLenWord-4);
if(!unlistDict.IsExist(sLastPart,-1))
strcpy(sLastPart,m_sWords[i]+nLenWord-2);
}
else
{
strcpy(sLastPart,m_sWords[i]+nLenWord-2);
}
nLenPart=strlen(sLastPart);
if(nLenPart<nLenWord)
{//Get first part
strncpy(sFirstPart,m_sWords[i],nLenWord-nLenPart);
sFirstPart[nLenWord-nLenPart]=0;
}
else
{
strncpy(sFirstPart,m_sWords[i],nLenWord-2);
sFirstPart[nLenWord-2]=0;
strncpy(sLastPart,m_sWords[i]+nLenWord-2,2);
sLastPart[2]=0;
}
nFirstPOS=11;
nLastPOS=1;
}
else
{//Combination by Next word and last component
nLenWord=strlen(m_sWords[i]);
if(nLenWord>4)//Get last component
{
strncpy(sFirstPart,m_sWords[i],4);
sFirstPart[4]=0;
if(!unlistDict.IsExist(sFirstPart,-1))
sFirstPart[2]=0;
}
else
{
strncpy(sFirstPart,m_sWords[i],2);
sFirstPart[2]=0;
}
nLenPart=strlen(sFirstPart);
if(nLenPart<nLenWord)
{//Get first part
strncpy(sLastPart,m_sWords[i]+nLenPart,nLenWord-nLenPart);
sLastPart[nLenWord-nLenPart]=0;
}
else
{
strncpy(sFirstPart,m_sWords[i],2);
sFirstPart[2]=0;
strncpy(sLastPart,m_sWords[i]+2,nLenWord-2);
sLastPart[nLenWord-2]=0;
}
if(unlistDict.IsExist(sFirstPart,1)&&m_nBestTag[i-1]==5)
//小陈说：
nFirstPOS=1;
else if(unlistDict.IsExist(m_sWords[i-1],1)&&!unlistDict.IsExist(m_sWords[i-2],1))
nFirstPOS=4;
else
nFirstPOS=3;
nLastPOS=12;
}
strcpy(m_sWords[i],sFirstPart);
m_nBestTag[i]=nFirstPOS;
strcpy(m_sWords[i+1],sLastPart);
m_nBestTag[i+1]=nLastPOS;
m_nWordPosition[i+1]=m_nWordPosition[i]+strlen(sFirstPart);
}
}
return true;
}
bool CSpan::TransRecognize(CDictionary &dictCore,CDictionary &transDict)
{
char sPOS[MAX_WORDS_PER_SENTENCE]="Z";
int nStart=1,nEnd=1,i=1;
while(m_nBestTag[i]>-1)
{
if(m_nBestTag[i]==1||m_nBestTag[i]==11||m_nBestTag[i]==21)//1,11,21 Trigger the recognition
{
nStart=i;
nEnd=nStart+1;
while(m_nBestTag[nEnd]==m_nBestTag[nStart])//1,11,21
nEnd++;
while(m_nBestTag[nEnd]==m_nBestTag[nStart]+1)//2,12,22
nEnd++;
while(m_nBestTag[nEnd]==m_nBestTag[nStart]+2)//3,13,23
nEnd++;
while(m_nBestTag[nEnd]==30)//3,13,23
nEnd++;
}
else if(m_nBestTag[i]==2||m_nBestTag[i]==12||m_nBestTag[i]==22)//1,11,21 Trigger the recognition
{
nStart=i;
nEnd=nStart+1;
while(m_nBestTag[nEnd]==m_nBestTag[nStart])//2,12,22
nEnd++;
while(m_nBestTag[nEnd]==m_nBestTag[nStart]+1)//2,12,22
nEnd++;
while(m_nBestTag[nEnd]==30)//3,13,23
nEnd++;
}
if(nEnd>nStart&&!IsAllNum((unsigned char *)m_sWords[nStart])&&(nEnd>nStart+2||(nEnd==nStart+2&&(m_nBestTag[nEnd-1]!=30||strlen(m_sWords[nStart])>2))||(nEnd==nStart+1&&strlen(m_sWords[nStart])>2&&!dictCore.IsExist(m_sWords[nStart],-1))))
{
m_nUnknownWords[m_nUnknownIndex][0]=m_nWordPosition[nStart];
m_nUnknownWords[m_nUnknownIndex][1]=m_nWordPosition[nEnd];
m_dWordsPossibility[m_nUnknownIndex++]=ComputePossibility(nStart,nEnd-nStart+1,transDict);
nStart=nEnd;
}
if(i<nEnd)
i=nEnd;
else
i=i+1;
}
return true;
}
int CSpan::GetFrom(PWORD_RESULT pWordItems,int nIndex,CDictionary &dictCore, CDictionary &dictUnknown)
{
int nCount,aPOS[MAX_POS_PER_WORD],aFreq[MAX_POS_PER_WORD];
int nFreq=0,j,nRetPos=0,nWordsIndex=0;
bool bSplit=false;//Need to split in Transliteration recognition
int i=1;
nWordsIndex=i+nIndex-1;
for(;i<MAX_WORDS_PER_SENTENCE&&pWordItems[nWordsIndex].sWord[0]!=0;i++)
{
if(m_tagType==TT_NORMAL||!dictUnknown.IsExist(pWordItems[nWordsIndex].sWord,44))
{
strcpy(m_sWords[i],pWordItems[nWordsIndex].sWord);//store current word
m_nWordPosition[i+1]=m_nWordPosition[i]+strlen(m_sWords[i]);
}
else
{
if(!bSplit)
{
strncpy(m_sWords[i],pWordItems[nWordsIndex].sWord,2);//store current word
m_sWords[i][2]=0;
bSplit=true;
}
else
{
unsigned int nLen=strlen(pWordItems[nWordsIndex].sWord+2);
strncpy(m_sWords[i],pWordItems[nWordsIndex].sWord+2,nLen);//store current word
m_sWords[i][nLen]=0;
bSplit=false;
}
m_nWordPosition[i+1]=m_nWordPosition[i]+strlen(m_sWords[i]);
}
//Record the position of current word
m_nStartPos=m_nWordPosition[i+1];
//Move the Start POS to the ending
if(m_tagType!=TT_NORMAL)
{
//Get the POSs from the unknown recognition dictionary
dictUnknown.GetHandle(m_sWords[i],&nCount,aPOS,aFreq);
for(j=0;j<nCount;j++)
{//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i][j]=aPOS[j];
m_dFrequency[i][j]=-log((double)(1+aFreq[j]))+log((double)(m_context.GetFrequency(0,aPOS[j])+1));
}
//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
dictCore.GetHandle(m_sWords[i],&nCount,aPOS,aFreq);
nFreq=0;
for(int k=0;k<nCount;k++)
{
nFreq+=aFreq[k];
}
if(nCount>0)
{
m_nTags[i][j]=0;
//m_dFrequency[i][j]=(double)(1+nFreq)/(double)(m_context.GetFrequency(0,0)+1);
m_dFrequency[i][j]=-log((double)(1+nFreq))+log((double)(m_context.GetFrequency(0,0)+1));
j++;
}
}
else//For normal POS tagging
{
j=0;
//Get the POSs from the unknown recognition dictionary
if(pWordItems[nWordsIndex].nHandle>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].nHandle;
m_dFrequency[i][j]=pWordItems[nWordsIndex].dValue-LOG_MAX_FRQUENCE+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].nHandle<0)
{//The word has is only one POS value
//We have record its POS and nFrequncy in the items.
if(pWordItems[nWordsIndex].nHandle==-'t'*256-'t')//tt
{
char sWordOrg[100],sPostfix[10];
double dRatio=0.6925;//The ratio which transliteration as a person name
PostfixSplit(pWordItems[nWordsIndex].sWord,sWordOrg,sPostfix);
if(sPostfix[0]!=0)
dRatio=0.01;
m_nTags[i][j]='n'*256+'r';
m_dFrequency[i][j]=-log(dRatio)+pWordItems[nWordsIndex].dValue;
//m_dFrequency[i][j]=log(dRatio)+pWordItems[nWordsIndex].dValue-log(m_context.GetFrequency(0,m_nTags[i][j]))+log(MAX_FREQUENCE);
//P(W|R)=P(WRT)/P(RT)=P(R)*P(W|T)/P(R|T)
j++;
m_nTags[i][j]='n'*256+'s';
m_dFrequency[i][j]=-log(1-dRatio)+pWordItems[nWordsIndex].dValue;
//m_dFrequency[i][j]=log(1-dRatio)+pWordItems[nWordsIndex].dValue-log(m_context.GetFrequency(0,m_nTags[i][j]))+log(MAX_FREQUENCE);
j++;
}
else//Unknown words such as Chinese person name or place name
{
m_nTags[i][j]=-pWordItems[nWordsIndex].nHandle;
// m_dFrequency[i][j++]=(double)(1+pWordItems[nWordsIndex].nFrequency)/(double)(m_context.GetFrequency(0,aPOS[j])+1);
m_dFrequency[i][j++]=pWordItems[nWordsIndex].dValue;
}
}
dictCore.GetHandle(m_sWords[i],&nCount,aPOS,aFreq);
for(;j<nCount;j++)
{//Get the POS set of sCurWord in the unknown dictionary
m_nTags[i][j]=aPOS[j];
m_dFrequency[i][j]=-log((double)1+aFreq[j])+log((double)m_context.GetFrequency(0,m_nTags[i][j])+1);
}
}
}
if(j==0)
{//We donot know the POS, so we have to guess them according lexical knowledge
GuessPOS(i,&j);//Guess the POS of current word
}
m_nTags[i][j]=-1;//Set the ending POS
if(j==1)//No ambuguity
{//No ambuguity, so we can break from the loop
i++;
m_sWords[i][0]=0;
break;
}
if(!bSplit)
{nWordsIndex++;}
}
if(pWordItems[nWordsIndex].sWord[0]==0)
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!=TT_NORMAL)
m_nTags[i][0]=101;
else
m_nTags[i][0]=1;
m_dFrequency[i][0]=0;
m_sWords[i][0]=0;//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
}
