//逆向最长匹配
public string BackSplitting(RowFirstDynamicArray <ChainContent> m_segGraph)
{
string abc = "";
int nCol = m_segGraph.ColumnCount - 1;
int nRow = m_segGraph.RowCount - 1;
//ChainItem<ChainContent> dfg = m_segGraph.GetElement(m_segGraph.RowCount-1, m_segGraph.ColumnCount-1);
//List<ChainItem<ChainContent>> ab = new List<ChainItem<ChainContent>>();
while (nCol > 1)
{
for (int i = 0; i <= nRow; i++)
{
if (null != m_segGraph.GetElement(i, nCol))
{
if (abc == "")
{
abc = m_segGraph.GetElement(i, nCol).Content.sWord;
}
else
{
abc = m_segGraph.GetElement(i, nCol).Content.sWord + "/" + abc;
} nCol = i;
break;
}
}
}
return(abc);
}
//逆向最长匹配
public string BackSplitting(RowFirstDynamicArray<ChainContent> m_segGraph)
{
string abc = "";
int nCol = m_segGraph.ColumnCount-1;
int nRow = m_segGraph.RowCount-1;
//ChainItem<ChainContent> dfg = m_segGraph.GetElement(m_segGraph.RowCount-1, m_segGraph.ColumnCount-1);
//List<ChainItem<ChainContent>> ab = new List<ChainItem<ChainContent>>();
while (nCol > 1)
{
for (int i = 0; i <=nRow; i++)
{
if (null != m_segGraph.GetElement(i, nCol))
{
if (abc == "") { abc = m_segGraph.GetElement(i, nCol).Content.sWord; }
else{
abc = m_segGraph.GetElement(i, nCol).Content.sWord + "/" + abc;
}nCol = i;
break;
}
}
}
return abc;
}
//正向最长匹配
public string ForwardSplitting(RowFirstDynamicArray<ChainContent> m_segGraph)
{
string abc = "";
// =GetSegGraph();
int currcol = 0;
ChainItem<ChainContent> dfg= m_segGraph.GetElement(0, 1);
ChainItem<ChainContent> aa = dfg.next;
while (null != aa.next)
{
if (aa.next.row == aa.row)
{
currcol = aa.next.col;
aa = aa.next;
}
else
{
abc += aa.Content.sWord ;
currcol = aa.col;
aa = m_segGraph.GetFirstElementOfRow(currcol);
break;
}
}
while (null != aa.next)
{
if (aa.next.row == aa.row)
{
currcol = aa.next.col;
aa = aa.next;
}
else
{
abc += "/"+aa.Content.sWord ;
currcol = aa.col;
aa = m_segGraph.GetFirstElementOfRow(currcol);
}
}
return abc;
}
//正向最长匹配
public string ForwardSplitting(RowFirstDynamicArray <ChainContent> m_segGraph)
{
string abc = "";
// =GetSegGraph();
int currcol = 0;
ChainItem <ChainContent> dfg = m_segGraph.GetElement(0, 1);
ChainItem <ChainContent> aa = dfg.next;
while (null != aa.next)
{
if (aa.next.row == aa.row)
{
currcol = aa.next.col;
aa = aa.next;
}
else
{
abc += aa.Content.sWord;
currcol = aa.col;
aa = m_segGraph.GetFirstElementOfRow(currcol);
break;
}
}
while (null != aa.next)
{
if (aa.next.row == aa.row)
{
currcol = aa.next.col;
aa = aa.next;
}
else
{
abc += "/" + aa.Content.sWord;
currcol = aa.col;
aa = m_segGraph.GetFirstElementOfRow(currcol);
}
}
return(abc);
}
//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;
}
