///<summary>
///Inserts the specified constituent into this parse based on its text span. This
///method assumes that the specified constituent can be inserted into this parse.
///</summary>
///<param name="constituent">
///The constituent to be inserted.
///</param>
public virtual void Insert(Parse constituent)
{
Util.Span constituentSpan = constituent.mSpan;
if (mSpan.Contains(constituentSpan))
{
int currentPart;
int partCount = mParts.Count;
for (currentPart = 0; currentPart < partCount; currentPart++)
{
Parse subPart = mParts[currentPart];
Util.Span subPartSpan = subPart.mSpan;
if (subPartSpan.Start > constituentSpan.End)
{
break;
}
// constituent Contains subPart
else if (constituentSpan.Contains(subPartSpan))
{
mParts.RemoveAt(currentPart);
currentPart--;
constituent.mParts.Add(subPart);
subPart.Parent = constituent;
partCount = mParts.Count;
}
else if (subPartSpan.Contains(constituentSpan))
{
//System.Console.WriteLine("Parse.insert:subPart contains con");
subPart.Insert(constituent);
return;
}
}
mParts.Insert(currentPart, constituent);
constituent.Parent = this;
}
else
{
throw new ParseException("Inserting constituent not contained in the sentence!");
}
}
public virtual void AddEvents(Util.Span[] tokens, string input)
{
if (tokens.Length > 0)
{
int startPosition = tokens[0].Start;
int endPosition = tokens[tokens.Length - 1].End;
string sentence = input.Substring(startPosition, (endPosition) - (startPosition));
Util.Span[] candidateTokens = MaximumEntropyTokenizer.SplitOnWhitespaces(sentence);
int firstTrainingToken = -1;
int lastTrainingToken = -1;
for (int currentCandidate = 0; currentCandidate < candidateTokens.Length; currentCandidate++)
{
Util.Span candidateSpan = candidateTokens[currentCandidate];
string candidateToken = sentence.Substring(candidateSpan.Start, (candidateSpan.End) - (candidateSpan.Start));
//adjust candidateSpan to text offsets
candidateSpan = new Util.Span(candidateSpan.Start + startPosition, candidateSpan.End + startPosition);
//should we skip this token
if (candidateToken.Length > 1 && (!mSkipAlphanumerics || !MaximumEntropyTokenizer.AlphaNumeric.IsMatch(candidateToken)))
{
//find offsets of annotated tokens inside candidate tokens
bool foundTrainingTokens = false;
for (int currentToken = lastTrainingToken + 1; currentToken < tokens.Length; currentToken++)
{
if (candidateSpan.Contains(tokens[currentToken]))
{
if (!foundTrainingTokens)
{
firstTrainingToken = currentToken;
foundTrainingTokens = true;
}
lastTrainingToken = currentToken;
}
else if (candidateSpan.End < tokens[currentToken].End)
{
break;
}
else if (tokens[currentToken].End < candidateSpan.Start)
{
//keep looking
}
else
{
throw new ApplicationException("Bad training token: " + tokens[currentToken] + " cand: " + candidateSpan);
}
}
// create training data
if (foundTrainingTokens)
{
for (int currentToken = firstTrainingToken; currentToken <= lastTrainingToken; currentToken++)
{
Util.Span trainingTokenSpan = tokens[currentToken];
int candidateStart = candidateSpan.Start;
for (int currentPosition = trainingTokenSpan.Start + 1; currentPosition < trainingTokenSpan.End; currentPosition++)
{
string[] context = mContextGenerator.GetContext(new Tuple <string, int>(candidateToken, currentPosition - candidateStart));
mEvents.Add(new SharpEntropy.TrainingEvent(TokenContextGenerator.NoSplitIndicator, context));
}
if (trainingTokenSpan.End != candidateSpan.End)
{
string[] context = mContextGenerator.GetContext(new Tuple <string, int>(candidateToken, trainingTokenSpan.End - candidateStart));
mEvents.Add(new SharpEntropy.TrainingEvent(TokenContextGenerator.SplitIndicator, context));
}
}
}
}
}
}
}
public virtual void AddEvents(Util.Span[] tokens, string input)
{
if (tokens.Length > 0)
{
int startPosition = tokens[0].Start;
int endPosition = tokens[tokens.Length - 1].End;
string sentence = input.Substring(startPosition, (endPosition) - (startPosition));
Util.Span[] candidateTokens = MaximumEntropyTokenizer.Split(sentence);
int firstTrainingToken = -1;
int lastTrainingToken = -1;
for (int currentCandidate = 0; currentCandidate < candidateTokens.Length; currentCandidate++)
{
Util.Span candidateSpan = candidateTokens[currentCandidate];
string candidateToken = sentence.Substring(candidateSpan.Start, (candidateSpan.End) - (candidateSpan.Start));
//adjust candidateSpan to text offsets
candidateSpan = new Util.Span(candidateSpan.Start + startPosition, candidateSpan.End + startPosition);
//should we skip this token
if (candidateToken.Length > 1 && (!mSkipAlphanumerics || !MaximumEntropyTokenizer.AlphaNumeric.IsMatch(candidateToken)))
{
//find offsets of annotated tokens inside candidate tokens
bool foundTrainingTokens = false;
for (int currentToken = lastTrainingToken + 1; currentToken < tokens.Length; currentToken++)
{
if (candidateSpan.Contains(tokens[currentToken]))
{
if (!foundTrainingTokens)
{
firstTrainingToken = currentToken;
foundTrainingTokens = true;
}
lastTrainingToken = currentToken;
}
else if (candidateSpan.End < tokens[currentToken].End)
{
break;
}
else if (tokens[currentToken].End < candidateSpan.Start)
{
//keep looking
}
else
{
throw new ApplicationException("Bad training token: " + tokens[currentToken] + " cand: " + candidateSpan);
}
}
// create training data
if (foundTrainingTokens)
{
for (int currentToken = firstTrainingToken; currentToken <= lastTrainingToken; currentToken++)
{
Util.Span trainingTokenSpan = tokens[currentToken];
int candidateStart = candidateSpan.Start;
for (int currentPosition = trainingTokenSpan.Start + 1; currentPosition < trainingTokenSpan.End; currentPosition++)
{
string[] context = mContextGenerator.GetContext(new Util.Pair<string, int>(candidateToken, currentPosition - candidateStart));
mEvents.Add(new SharpEntropy.TrainingEvent(TokenContextGenerator.NoSplitIndicator, context));
}
if (trainingTokenSpan.End != candidateSpan.End)
{
string[] context = mContextGenerator.GetContext(new Util.Pair<string, int>(candidateToken, trainingTokenSpan.End - candidateStart));
mEvents.Add(new SharpEntropy.TrainingEvent(TokenContextGenerator.SplitIndicator, context));
}
}
}
}
}
}
}