//Unknown word recognition
//pWordSegResult:word Segmentation result;graphOptimum: The optimized segmentation graph
//graphSeg: The original segmentation graph
bool CUnknowWord::Recognition(PWORD_RESULT pWordSegResult, CDynamicArray &graphOptimum,CSegGraph &graphSeg,CDictionary &dictCore)
{
int nStartPos=0,j=0,nAtomStart,nAtomEnd,nPOSOriginal;
ELEMENT_TYPE 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_nUnknownIndex;i++)
{
while((unsigned int)j<graphSeg.m_nAtomCount&&nStartPos<m_roleTag.m_nUnknownWords[i][0])
{
nStartPos+=graphSeg.m_nAtomLength[j++];
}
nAtomStart=j;
while((unsigned int)j<graphSeg.m_nAtomCount&&nStartPos<m_roleTag.m_nUnknownWords[i][1])
{
nStartPos+=graphSeg.m_nAtomLength[j++];
}
nAtomEnd=j;
if(nAtomStart<nAtomEnd)
{
graphOptimum.GetElement(nAtomStart,nAtomEnd,&dValue,&nPOSOriginal);
if(dValue>m_roleTag.m_dWordsPossibility[i])//Set the element with less frequency
graphOptimum.SetElement(nAtomStart,nAtomEnd,m_roleTag.m_dWordsPossibility[i],m_nPOS,m_sUnknownFlags);
}
}
return true;
}
//CDynamicArray &aWord: the words array
//CDynamicArray &aWordBinaryNet:the net between words
//double dSmoothingPara: the parameter of data smoothing
//CDictionary &DictBinary: the binary dictionary
//CDictionary &DictCore: the Core dictionary
bool CSegment::BiGraphGenerate(CDynamicArray &aWord, CDynamicArray &aBinaryWordNet,double dSmoothingPara,CDictionary &DictBinary,CDictionary &DictCore)
{
PARRAY_CHAIN pTail,pCur,pNextWords;//Temp buffer
unsigned int nWordIndex=0,nTwoWordsFreq=0,nCurWordIndex,nNextWordIndex;
//nWordIndex: the index number of current word
double dCurFreqency,dValue,dTemp;
char sTwoWords[WORD_MAXLENGTH];
m_nWordCount=aWord.GetTail(&pTail);//Get tail element and return the words count
if(m_npWordPosMapTable)
{//free buffer
delete [] m_npWordPosMapTable;
m_npWordPosMapTable=0;
}
if(m_nWordCount>0)//Word count is greater than 0
{
m_npWordPosMapTable=new int[m_nWordCount];//Record the position of possible words
memset(m_npWordPosMapTable,0,m_nWordCount*sizeof(int));
}
pCur=aWord.GetHead();
while(pCur!=NULL)//Set the position map of words
{
m_npWordPosMapTable[nWordIndex++]=pCur->row*MAX_SENTENCE_LEN+pCur->col;
pCur=pCur->next;
}
pCur=aWord.GetHead();
while(pCur!=NULL)//
{
if(pCur->nPOS>=0)//It's not an unknown words
dCurFreqency=pCur->value;
else//Unknown words
dCurFreqency=DictCore.GetFrequency(pCur->sWord,2);
aWord.GetElement(pCur->col,-1,pCur,&pNextWords);//Get next words which begin with pCur->col
while(pNextWords&&pNextWords->row==pCur->col)//Next words
{
//Current words frequency
strcpy(sTwoWords,pCur->sWord);
strcat(sTwoWords,WORD_SEGMENTER);
strcat(sTwoWords,pNextWords->sWord);
nTwoWordsFreq=DictBinary.GetFrequency(sTwoWords,3);
//Two linked Words frequency
dTemp=(double)1/MAX_FREQUENCE;
//Smoothing
dValue=-log(dSmoothingPara*(1+dCurFreqency)/(MAX_FREQUENCE+80000)+(1-dSmoothingPara)*((1-dTemp)*nTwoWordsFreq/(1+dCurFreqency)+dTemp));
//-log{a*P(Ci-1)+(1-a)P(Ci|Ci-1)} Note 0<a<1
if(pCur->nPOS<0)//Unknown words: P(Wi|Ci);while known words:1
dValue+=pCur->value;
//Get the position index of current word in the position map table
nCurWordIndex=BinarySearch(pCur->row*MAX_SENTENCE_LEN+pCur->col,m_npWordPosMapTable,m_nWordCount);
nNextWordIndex=BinarySearch(pNextWords->row*MAX_SENTENCE_LEN+pNextWords->col,m_npWordPosMapTable,m_nWordCount);
aBinaryWordNet.SetElement(nCurWordIndex,nNextWordIndex,dValue,pCur->nPOS);
pNextWords=pNextWords->next;//Get next word
}
pCur=pCur->next;
}
return true;
}
