//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);
}
