// [Explicit-Dispose]
public void Dispose()
{
if (_wn != null)
{
_wn.Dispose();
}
_tokenizer = null;
_sentenceDetector = null;
_posTagger = null;
_chunker = null;
// Dispose CLI/C++ Dll
ap = null;
// Dispose all KB plugins
if (PlugInsNumber > 0)
{
for (int i = 0; i < PlugInsNumber; i++)
{
KBDrivers[i] = null;
KBDriversQueryPointers[i] = null;
}
}
}
public TextSentenceSplitter()
{
var fullPath = Path.Combine(GetBinariesPath(), "Data", "EnglishSD.nbin");
SaveResourceToFile(fullPath, "kyciti.Data.EnglishSD.nbin");
_maximumEntropySentenceDetector = new EnglishMaximumEntropySentenceDetector(fullPath);
}
public WordNetBoostrap(string nlpModelsPath, string wordNetPath)
{
this._wordNetPath = wordNetPath;
_wn = new WordNetEngine(_wordNetPath, true);
_tokenizer = new EnglishRuleBasedTokenizer(false);
_sentenceDetector = new EnglishMaximumEntropySentenceDetector(nlpModelsPath + "EnglishSD.nbin");
_posTagger = new EnglishMaximumEntropyPosTagger(nlpModelsPath + "EnglishPOS.nbin", nlpModelsPath + @"\Parser\tagdict");
}
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));
}
public void Dispose()
{
_tokenizer = null;
_sentenceDetector = null;
_posTagger = null;
SynsetArray = null;
IsStopWord = null;
_wn.Dispose();
}
// Defualt Constructor
public SemCluster(string DataFolder)
{
try
{
Console.WriteLine("\tSemCluster Text Analytics Tool");
Console.WriteLine("\t------------------------------");
Console.WriteLine("\t-Wikipedia local server couldn't be found!");
Console.WriteLine("\t-Seeds SemAve is in manual mode!");
Console.WriteLine();
Console.WriteLine();
Console.WriteLine("-> Resources loading ...");
Console.WriteLine();
#region Loading External Resources
_wn = new WordNetEngine(DataFolder + "WordNet", InMemoryWordNet);
_tokenizer = new EnglishRuleBasedTokenizer(TokenizeHyphen);
_sentenceDetector = new EnglishMaximumEntropySentenceDetector(DataFolder + "EnglishSD.nbin");
_posTagger = new EnglishMaximumEntropyPosTagger(DataFolder + "EnglishPOS.nbin", DataFolder + "\\Build\\tagdict");
_chunker = new EnglishTreebankChunker(DataFolder + "EnglishChunk.nbin");
#endregion
PlugInsManager(DataFolder);
Console.WriteLine("\tResources loaded successfully");
Console.WriteLine("\t" + PlugInsNumber + " KB plug-ins found in the repository");
Console.WriteLine("\tPress any key to continue ...");
Console.ReadKey();
Console.WriteLine();
RootVirtualNode = _wn.GetSynSet("Noun:1740");
ap = new AffinityPropagationClustering();
SynSetRelationTypes = new WordNetApi.Core.WordNetEngine.SynSetRelation[2];
SynSetRelationTypes[0] = WordNetApi.Core.WordNetEngine.SynSetRelation.Hypernym;
SynSetRelationTypes[1] = WordNetApi.Core.WordNetEngine.SynSetRelation.InstanceHypernym;
}
catch (Exception ex)
{
Dispose();
throw new Exception(ex.Message);
}
}
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.");
}