public string ShallowParse(string input)
{
var output = new StringBuilder();
string[] sentences = _sentenceDetector.SentenceDetect(input);
foreach (string sentence in sentences)
{
string[] tokens = _tokenizer.Tokenize(sentence);
string[] tags = _posTagger.Tag(tokens);
output.Append(string.Join(" ", _chunker.GetChunks(tokens, tags)));
output.Append("\r\n\r\n");
}
return(output.ToString());
}
public string[] SplitSentences(string paragraph)
{
if (mSentenceDetector == null)
{
mSentenceDetector = new EnglishMaximumEntropySentenceDetector(mModelPath + @"\EnglishSD.nbin");
}
return(mSentenceDetector.SentenceDetect(paragraph));
}
// NLP methods -------------------------------------------
private string[] SplitSentences(string paragraph)
{
if (_sentenceDetector == null)
{
_sentenceDetector = new EnglishMaximumEntropySentenceDetector(_modelPath + "EnglishSD.nbin");
}
return(_sentenceDetector.SentenceDetect(paragraph));
}
private string Lexicalize(string input)
{
var output = new StringBuilder();
string[] sentences = _sentenceDetector.SentenceDetect(input);
foreach (string sentence in sentences)
{
string[] tokens = _tokenizer.Tokenize(sentence);
string[] tags = _posTagger.Tag(tokens);
for (int currentTag = 0; currentTag < tags.Length; currentTag++)
{
if (!IsStopWord.ContainsKey(tokens[currentTag]))
{
if (tags[currentTag].StartsWith("NN"))
{
if (tags[currentTag] == "NNS")
{
output.Append(_wn.Lemmatize(tokens[currentTag], "noun")).Append(" ");
}
else
{
output.Append(tokens[currentTag]).Append(" ");
}
}
else
if (tags[currentTag].StartsWith("VB"))
{
if (tags[currentTag] != "VBP")
{
output.Append(_wn.Lemmatize(tokens[currentTag], "verb")).Append(" ");
}
}
else
if (tags[currentTag].StartsWith("JJ"))
{
output.Append(tokens[currentTag]).Append(" ");
}
}
}
}
return(output.ToString());
}
public string[] SplitTextToSentences(string text)
{
return(_maximumEntropySentenceDetector.SentenceDetect(text));
}
private static void OptimizeSentenceDetectionTraining()
{
// all directories in Input folder
var inputFolderPath = CurrentDirectory + "Input/";
var allDirectories = Directory.GetDirectories(inputFolderPath);
Console.WriteLine("Pick the model to train:");
for (var i = 0; i < allDirectories.Length; i++)
{
Console.WriteLine("{0} - {1}", i, Path.GetFileName(allDirectories[i]));
}
// read directory chosen by user
int directoryIndexPicked = LoopUntilValidUserInput(input => int.Parse(input),
i => i < allDirectories.Length, string.Format("Please enter a number in [0..{0}]", allDirectories.Length - 1));
// read user parameters
Console.WriteLine("Enter the iteration values to test, separated by a comma (ex: 10,100,200)");
var iterations = LoopUntilValidUserInput(input => input.Split(',').Select(s => int.Parse(s.Trim())).ToList(),
li => li != null && li.Any(),
"At least one iteration value is required");
Console.WriteLine("Enter the cut values to test, separated by a comma (ex: 1,2,5)");
var cuts = LoopUntilValidUserInput(input => input.Split(',').Select(s => int.Parse(s.Trim())).ToList(),
li => li != null && li.Any(),
"At least one cut value is required");
// train model file
var directory = allDirectories[directoryIndexPicked];
var allTrainFiles = Directory.GetFiles(directory, "*.train");
Console.WriteLine("Training model with files {0}", string.Join(", ", allTrainFiles.Select(f => Path.GetFileNameWithoutExtension(f))));
// load training data
var allSentences = new List <string>();
foreach (var file in allTrainFiles.Where(f => !f.Contains("wsj")))
{
allSentences.AddRange(File.ReadAllLines(file));
}
var testData = string.Join(" ", allSentences);
var bestIterationValue = iterations.First();
var bestCutValue = iterations.First();
var bestAccuracy = 0.0d;
var endOfSentenceScanner = new CharactersSpecificEndOfSentenceScanner('.', '?', '!', '"', '-', '…');
foreach (var iteration in iterations)
{
foreach (var cut in cuts)
{
var model = MaximumEntropySentenceDetector.TrainModel(allTrainFiles, iteration, cut, endOfSentenceScanner);
// compute accuracy
var sentenceDetector = new MaximumEntropySentenceDetector(model, endOfSentenceScanner);
var results = sentenceDetector.SentenceDetect(testData);
// not perfect for comparing files but it gives a very good approximation
//var commonValues = allSentences.Intersect(results).Count();
var nbOfCommonSentences = 0;
foreach (var result in results)
{
if (allSentences.Contains(result))
{
nbOfCommonSentences++;
}
else
{
//Console.WriteLine(result);
}
}
var accuracyScore = (float)2 * nbOfCommonSentences / (allSentences.Count + results.Count());
Console.WriteLine("Accuracy for iteration={0} and cut={1}: {2}", iteration, cut, accuracyScore);
if (accuracyScore > bestAccuracy)
{
bestAccuracy = accuracyScore;
bestIterationValue = iteration;
bestCutValue = cut;
}
}
}
// Persit model
var outputFilePath = CurrentDirectory + "Output/" + Path.GetFileName(directory) + ".nbin";
Console.WriteLine("Persisting model for iteration={0} and cut={1} to file '{2}'...", bestIterationValue, bestCutValue, outputFilePath);
var bestModel = MaximumEntropySentenceDetector.TrainModel(allTrainFiles, bestIterationValue, bestCutValue, endOfSentenceScanner);
new BinaryGisModelWriter().Persist(bestModel, outputFilePath);
Console.WriteLine("Output file written.");
}