/// <summary>
/// Split the input content to individual words
/// </summary>
/// <param name="contents">Content to split into words</param>
/// <returns></returns>
public static IEnumerable <string> ExtractSentences(string contents)
{
var sentenizer = new SentenceDetectorME(Model);
var sentences = sentenizer.sentDetect(contents);
return(sentences);
}
static void Main(string[] args)
{
String[] file = Directory.GetFiles(@"..\..\..\", "*.txt");
StreamWriter sw = new StreamWriter(@"..\..\..\output\Html.txt");
foreach (String files in file)
{
using (StreamReader sr = new StreamReader(files))
{
while (sr.Peek() != -1)
{
string line = sr.ReadLine();
int i;
java.io.InputStream modelin = new java.io.FileInputStream(string.Format(@"{0}\en-sent.bin", @"..\Debug"));
SentenceModel model = new SentenceModel(modelin);
SentenceDetector detector = new SentenceDetectorME(model);
string[] sents = detector.sentDetect(line);
foreach (var sent in sents)
{
sw.WriteLine(sent);
}
}
}
sw.Flush();
}
sw.Close();
}
/// <summary>
/// Split the input content to individual words
/// </summary>
/// <param name="contents">Content to split into words</param>
/// <returns></returns>
public static IEnumerable<string> ExtractSentences(string contents)
{
var sentenizer = new SentenceDetectorME(Model);
var sentences = sentenizer.sentDetect(contents);
return sentences;
}
public void TestEverything()
{
using (var file = Tests.OpenFile("/opennlp/tools/sentdetect/Sentences.txt")) {
var mlParams = new TrainingParameters();
mlParams.Set(Parameters.Iterations, "100");
mlParams.Set(Parameters.Cutoff, "0");
var sdFactory = new SentenceDetectorFactory("en", true, null, null);
var stream = new SentenceSampleStream(new PlainTextByLineStream(file));
var model = SentenceDetectorME.Train("en", stream, sdFactory, mlParams);
Assert.AreEqual("en", model.Language);
Assert.AreEqual(model.UseTokenEnd, true);
var sMe = new SentenceDetectorME(model);
// test the SharpNL sentences
SentenceDetectorMETest.EvalSentences(sMe);
var sFile = Path.GetTempFileName();
model.Serialize(new FileStream(sFile, FileMode.Create));
var jModel2 = new JavaModel(OpenNLP.CreateInputStream(sFile));
var jMe = new JavaSDME(jModel2);
// test the Java OpenNLP sentences.
JavaEvalSentences(jMe);
// first try?! Yes! ;-)
}
}
private StringBuilder ReverseIt(string Message)
{
StringBuilder reversedString = new StringBuilder();
//lets do some language processing tasks to identify sentence structure
SentenceDetectorME sentenceParser = new SentenceDetectorME(LoadNLP.sentenceModel);
TokenizerME tokenizer = new TokenizerME(LoadNLP.tokenModel);
string[] sentences = sentenceParser.sentDetect(Message);
//iterate through each sentence
foreach (string sentence in sentences)
{
string[] tokens = tokenizer.tokenize(sentence);
//reverse the tokens
for (int i = 0; i < tokens.Length / 2; i++)
{
string storage = tokens[i];
tokens[i] = tokens[tokens.Length - i - 1];
tokens[tokens.Length - i - 1] = storage;
}
//Now that we've organized the sentence nicely, lets detokenize it and convert back to a usable string
reversedString.Append(DeTokenize(tokens, DetokenizationDictionary.Operation.MOVE_LEFT));
}
return(reversedString);
}
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, false);
if (mlParams != null)
{
if (TrainUtil.isSequenceTraining(mlParams.Settings))
{
throw new TerminateToolException(1, "Sequence training is not supported!");
}
}
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
Jfile modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("sentence detector model", modelOutFile);
char[] eos = null;
if (@params.EosChars != null)
{
eos = @params.EosChars.ToCharArray();
}
SentenceModel model;
try
{
Dictionary dict = loadDict(@params.AbbDict);
SentenceDetectorFactory sdFactory = SentenceDetectorFactory.create(@params.Factory, @params.Lang, true, dict, eos);
model = SentenceDetectorME.train(@params.Lang, sampleStream, sdFactory, mlParams);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("sentence detector", modelOutFile, model);
}
private void LoadSentenceDetector()
{
if (!alreadyLoadSentenceDetector)
{
java.io.FileInputStream modelInpStream = new java.io.FileInputStream("Resources\\en-sent.bin");
SentenceModel sentenceModel = new SentenceModel(modelInpStream);
sentenceDetector = new SentenceDetectorME(sentenceModel);
alreadyLoadSentenceDetector = true;
}
}
public Span[] SentPosDetect(string paragraph)
{
var bin = GetFileStream("en-sent.bin");
SentenceModel model = new SentenceModel(bin);
SentenceDetectorME sdetector = new SentenceDetectorME(model);
Span[] sentences = sdetector.sentPosDetect(paragraph);
bin.close();
return(sentences);
}
public static SentenceModel TrainModel(string path)
{
FileStream fs = new FileStream(path, FileMode.Open, FileAccess.Read);
TrainingParameters trainParams = new TrainingParameters();
trainParams.Set(Parameters.Iterations, "100");
trainParams.Set(Parameters.Cutoff, "0");
SentenceDetectorFactory detectorFactory = new SentenceDetectorFactory(TRAINING_LANGUAGE, true, null, null);
SentenceSampleStream sampleStream = new SentenceSampleStream(new PlainTextByLineStream(fs));
return(SentenceDetectorME.Train(TRAINING_LANGUAGE, sampleStream, detectorFactory, trainParams));
}
static void Sent(string src)
{
StreamReader input = new StreamReader(src);
StreamWriter output = new StreamWriter(Regex.Replace(src, "(.*).txt", "$1_sent.txt"));
while (input.Peek() != -1)
{
InputStream modelIn = new FileInputStream(@"..\..\..\en-sent.bin");
SentenceModel smodel = new SentenceModel(modelIn);
SentenceDetector detector = new SentenceDetectorME(smodel);
string[] sents = detector.sentDetect(input.ReadLine());
foreach (string sent in sents)
{
output.WriteLine(sent);
}
}
}
public void AbbreviationDefaultBehaviorTest()
{
var samples =
"Test E-mail met zowel letsel als 12. Toedracht in het onderwerp." + Environment.NewLine +
"Dit is een 2e regel met een tel. 011-4441444 erin." + Environment.NewLine +
"Dit is een 2e regel." + Environment.NewLine +
"Dit is een 2e regel." + Environment.NewLine + Environment.NewLine +
"Dit is een 2e regel met een tel. 033-1333123 erin!" + Environment.NewLine +
"Test E-mail met zowel winst als 12. toedracht in het onderwerp." + Environment.NewLine +
"Dit is een 2e regel!" + Environment.NewLine +
"Dit is een 2e regel." + Environment.NewLine;
var stringsToIgnoreDictionary = new SharpNL.Dictionary.Dictionary(false)
{
{ "12. Toedracht" },
{ "Tel." },
};
var trainingParameters = new TrainingParameters();
trainingParameters.Set(Parameters.Algorithm, "MAXENT");
trainingParameters.Set(Parameters.TrainerType, "Event");
trainingParameters.Set(Parameters.Iterations, "100");
trainingParameters.Set(Parameters.Cutoff, "5");
char[] eos = { '.', '?', '!' };
var sdFactory = new SentenceDetectorFactory("nl", true, stringsToIgnoreDictionary, eos);
var stringReader = new StringReader(samples);
var stream = new SentenceSampleStream(new PlainTextByLineStream(stringReader));
var sentenceModel = SentenceDetectorME.Train("nl", stream, sdFactory, trainingParameters);
var sentenceDetectorMe = new SentenceDetectorME(sentenceModel);
var sentences = sentenceDetectorMe.SentDetect(samples);
var expected = samples.Split(new [] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries);
Assert.AreEqual(8, sentences.Length);
for (var i = 0; i < sentences.Length; i++)
{
Assert.AreEqual(expected[i], sentences[i]);
}
}
public void TestSentenceDetector()
{
using (var file = Tests.OpenFile("/opennlp/tools/sentdetect/Sentences.txt")) {
var mlParams = new TrainingParameters();
mlParams.Set(Parameters.Iterations, "100");
mlParams.Set(Parameters.Cutoff, "0");
var sdFactory = new SentenceDetectorFactory("en", true, null, null);
var stream = new SentenceSampleStream(new PlainTextByLineStream(file));
var model = SentenceDetectorME.Train("en", stream, sdFactory, mlParams);
Assert.AreEqual("en", model.Language);
Assert.AreEqual(model.UseTokenEnd, true);
EvalSentences(new SentenceDetectorME(model));
}
}
public void SplitSentences()
{
var txt = File.ReadAllText(@"c:\dev\d-mill\uspe\Data\uspe-1.txt");
txt = Regex.Replace(txt, "\\s+", " ");
txt = Regex.Replace(txt, "\\r\\n", "");
txt = Regex.Replace(txt, "MR.\\s+", "MR.");
var modelStream = new java.io.FileInputStream("../../Models/en-sent.bin");
var model = new SentenceModel(modelStream);
var detector = new SentenceDetectorME(model);
var sentences = detector.sentDetect(txt);
File.WriteAllLines(@"c:\dev\d-mill\uspe\Data\uspe-sentenced.txt", sentences);
}
public static void Main(string[] args)
{
DirectoryInfo folder = new DirectoryInfo(@"..\..\..\..\..\Dataset");
foreach (var fname in folder.GetFiles())
{
String line = File.ReadAllText(fname.FullName);
java.io.InputStream modelIn = new java.io.FileInputStream(@"..\..\..\..\..\en-sent.bin");
SentenceModel smodel = new SentenceModel(modelIn);
SentenceDetector detector = new SentenceDetectorME(smodel);
string[] sents = detector.sentDetect(line);
using (StreamWriter sw = new StreamWriter(fname.FullName.Replace(fname.FullName.Substring(fname.FullName.Length - 3), "rtf")))
{
foreach (var sent in sents)
{
sw.WriteLine(sent);
}
}
}
}
/// <summary>
/// Perform sentence detection the input stream.
///
/// A newline will be treated as a paragraph boundary.
/// </summary>
public override void run(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine(Help);
}
else
{
SentenceModel model = (new SentenceModelLoader()).load(new File(args[0]));
SentenceDetectorME sdetector = new SentenceDetectorME(model);
ObjectStream <string> paraStream = new ParagraphStream(new PlainTextByLineStream(new InputStreamReader(Console.OpenStandardInput)));
PerformanceMonitor perfMon = new PerformanceMonitor(System.err, "sent");
perfMon.start();
try
{
string para;
while ((para = paraStream.read()) != null)
{
string[] sents = sdetector.sentDetect(para);
foreach (string sentence in sents)
{
Console.WriteLine(sentence);
}
perfMon.incrementCounter(sents.Length);
Console.WriteLine();
}
}
catch (IOException e)
{
CmdLineUtil.handleStdinIoError(e);
}
perfMon.stopAndPrintFinalResult();
}
}
public NLP()
{
//loading sentence detector model
java.io.FileInputStream modelInpStream = new java.io.FileInputStream("Resources\\en-sent.bin");
SentenceModel sentenceModel = new SentenceModel(modelInpStream);
sentenceDetector = new SentenceDetectorME(sentenceModel);
//loading tokenizer model
modelInpStream = new java.io.FileInputStream("Resources\\en-token.bin");
TokenizerModel tokenizerModel = new TokenizerModel(modelInpStream);
tokenizer = new TokenizerME(tokenizerModel);
modelInpStream = new java.io.FileInputStream("Resources\\en-pos-maxent.bin");
POSModel posModel = new POSModel(modelInpStream);
tagger = new POSTaggerME(posModel);
modelInpStream = new java.io.FileInputStream("Resources\\en-chunker.bin");
ChunkerModel chunkerModel = new ChunkerModel(modelInpStream);
chunker = new ChunkerME(chunkerModel);
modelInpStream = new java.io.FileInputStream("Resources\\en-parser-chunking.bin");
ParserModel parserModel = new ParserModel(modelInpStream);
parser = ParserFactory.create(parserModel);
//loading stop words list
StreamReader sr = new StreamReader("Resources\\english.stop.txt");
string line;
while ((line = sr.ReadLine()) != null)
{
stopwords.Add(Stemming(line));
stopwords.Add(line);
}
}
static void Main(string[] args)
{
StreamWriter sw = new StreamWriter(@"..\..\Data\result.txt");
StreamReader sr = new StreamReader(@"..\..\Data\data.txt");
while (sr.Peek() != -1)
{
string line = sr.ReadLine();
java.io.InputStream modelIn = new java.io.FileInputStream("en-sent.bin");
SentenceModel smodel = new SentenceModel(modelIn);
SentenceDetector detector = new SentenceDetectorME(smodel);
string[] sents = detector.sentDetect(line);
foreach (var sent in sents)
{
sw.WriteLine(sent);
sw.WriteLine();
}
sw.Flush();
}
sr.Close();
sw.Close();
}
static void Main(string[] args)
{
java.io.InputStream modelIn = new java.io.FileInputStream(string.Format("en-sent.bin"));
java.io.InputStream modelIn2 = new java.io.FileInputStream(string.Format("en-token.bin"));
TokenizerModel model = new TokenizerModel(modelIn2);
TokenizerME mE = new TokenizerME(model);
SentenceModel sM = new SentenceModel(modelIn);
SentenceDetector detector = new SentenceDetectorME(sM);
string folderName = @"C:\Users\Administrator\Desktop\lab-6-opennlp-ju-zi-qie-fen-10411174\file";
foreach (string fname in System.IO.Directory.GetFiles(folderName))
{
String line = null;
String[] name = fname.Split('\\');
StreamWriter sw = new StreamWriter(@"C:\Users\Administrator\Desktop\lab-6-opennlp-ju-zi-qie-fen-10411174\answer\" + name[6]);
StreamReader file2 = new StreamReader(fname);
while ((line = file2.ReadLine()) != null)
{
string str = null;
string[] sents = detector.sentDetect(line);
if (sents.Length.Equals(0))
{
continue;
}
foreach (var s in sents)
{
str = str + s;
}
var Tokens = mE.tokenize(str);
foreach (var s in Tokens)
{
sw.Write(s + " ");
}
sw.WriteLine();
}
sw.Close();
}
}
private static SentenceModel Train(SentenceDetectorFactory factory)
{
return(SentenceDetectorME.Train("en", CreateSampleStream(), factory, TrainingParameters.DefaultParameters()));
}
// Constructors and finalizers:
private Repository()
{
_assemblyName = Regex.Match(_assemblyFullName, "^(.*?),.*$").Result("$1");
_rootDrive = ("/usr/project/xtmp/dp195/Poetix18/").Replace(@"\", Dsc);
_nlpFolder = ("rhetorica/nlp/").Replace(@"\", Dsc);
_openNlpModelsFolder = ("OpenNLP/models/").Replace(@"\", Dsc);
_openNlpModelsPath = RootDrive + _nlpFolder + _openNlpModelsFolder;
_wordNetFolder = ("WordNet_3/").Replace(@"\", Dsc);
_wordNetPath = RootDrive + _nlpFolder + _wordNetFolder;
_grammarFolder = ("StanfordParser/grammar/").Replace(@"\", Dsc);
_grammarPath = RootDrive + _nlpFolder + _grammarFolder;
_dataFolder = ("data/").Replace(@"\", Dsc);
_nlpTextsPath = RootDrive + _dataFolder;
string[] localTextDirectoryParts =
{
CurrentAssemblyDirectoryPath,
"..", "..","..", "data"
//"..", "..", "text"
};
_localTextPath = Path.Combine(localTextDirectoryParts) + "/"; // For development use
// WordNet engine:
Console.Write("Loading WordNet engine.... ");
_wordNetEngine = new WordNetEngine(WordNetPath, true);
Console.WriteLine("Done.");
// OpenNLP sentence detector:
Console.Write("Loading OpenNLP sentence detector.... ");
java.io.FileInputStream modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-sent.bin");
_sentenceModel = new SentenceModel(modelInputStream);
modelInputStream.close();
_sentenceDetector = new SentenceDetectorME(_sentenceModel);
Console.WriteLine("Done.");
// OpenNLP tokenizer:
Console.Write("Loading OpenNLP tokenizer.... ");
modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-token.bin");
_tokenizerModel = new opennlp.tools.tokenize.TokenizerModel(modelInputStream);
modelInputStream.close();
_tokenizer = new opennlp.tools.tokenize.TokenizerME(_tokenizerModel);
Console.WriteLine("Done.");
// OpenNLP name finder:
Console.Write("Loading OpenNLP name finder.... ");
modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-ner-person.bin");
_tokenNameFinderModel = new TokenNameFinderModel(modelInputStream);
modelInputStream.close();
_nameFinder = new NameFinderME(_tokenNameFinderModel);
Console.WriteLine("Done.");
// OpenNLP POS tagger:
Console.Write("Loading OpenNLP POS tagger.... ");
modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-pos-maxent.bin");
_posModel = new POSModel(modelInputStream);
modelInputStream.close();
_tagger = new POSTaggerME(_posModel);
Console.WriteLine("Done.");
// OpenNLP chunker:
Console.Write("Loading OpenNLP chunker.... ");
modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-chunker.bin");
_chunkerModel = new ChunkerModel(modelInputStream);
modelInputStream.close();
_chunker = new ChunkerME(_chunkerModel);
Console.WriteLine("Done.");
// OpenNLP parser:
if (_loadParser)
{
Console.Write("Loading OpenNLP parser.... ");
modelInputStream = new java.io.FileInputStream(OpenNlpModelsPath + "en-parser-chunking.bin");
_parserModel = new ParserModel(modelInputStream);
modelInputStream.close();
_parser = ParserFactory.create(_parserModel);
Console.WriteLine("Done.");
}
// Stanford parser:
//_stanfordParser = new LexicalizedParser(GrammarPath + "englishPCFG.ser.gz"); // Obsolete method
_stanfordParser = LexicalizedParser.loadModel(GrammarPath + "englishPCFG.ser.gz");
// Porter stemmer:
_porterStemmer = new PorterStemmer();
}
public SentenceDetector(SentenceModel model)
{
this.detector = new SentenceDetectorME(model);
}
public SentenceDetector()
{
this.detector = new SentenceDetectorME(TrainModel(Environment.CurrentDirectory + TRAINING_MODEL_PATH));
}
public SentenceDetector(FileStream modelStream)
{
SentenceModel model = new SentenceModel(modelStream);
this.detector = new SentenceDetectorME(model);
}
public NLPSentenceDetectorOp()
{
sentenceSplitter = null;
}
public NLPSentenceDetectorOp(SentenceModel model)
{
sentenceSplitter = new SentenceDetectorME(model);
}
public String chooseSentenceMenu()
{
int userInputNumber = 0;
string userChoosenSentence = "";
System.Console.WriteLine("Choose a sentence to use from your current text");
System.Console.WriteLine("Must be a space between each sentence");
try
{
java.io.File file = new java.io.File("C:\\en-sent.bin");
java.io.InputStream modelIn = new FileInputStream("C:\\Users\\jcoleman\\Documents\\Capstone\\jcoleman_Capstone\\Code\\NEWCHATBOT\\ConsoleBot\\ConsoleBot\\en-sent.bin");
SentenceModel model = new SentenceModel(modelIn);
SentenceDetectorME sentenceDetector = new SentenceDetectorME(model);
string text = "";
FileText = System.IO.File.ReadAllLines(FilePath);
for (int i = 0; i < FileText.Length; i++)
{
text += FileText[i];
}
string[] sentences = sentenceDetector.sentDetect(text);
for(int s = 0;s < sentences.Length;s++)
{
System.Console.WriteLine((s+1) +" : " +sentences[s]);
}
string userInput = System.Console.ReadLine();
userInputNumber = int.Parse(userInput);
userChoosenSentence = sentences[userInputNumber - 1];
modelIn.close();
}
catch(Exception e)
{
System.Console.WriteLine(e.Message);
}
return userChoosenSentence;
}
internal static void EvalSentences(SentenceDetectorME sentDetect)
{
const string sampleSentences1 = "This is a test. There are many tests, this is the second.";
var sents = sentDetect.SentDetect(sampleSentences1);
Assert.AreEqual(sents.Length, 2);
Assert.AreEqual(sents[0], "This is a test.");
Assert.AreEqual(sents[1], "There are many tests, this is the second.");
var probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(probs.Length, 2);
const string sampleSentences2 = "This is a test. There are many tests, this is the second";
sents = sentDetect.SentDetect(sampleSentences2);
Assert.AreEqual(sents.Length, 2);
probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(probs.Length, 2);
Assert.AreEqual(sents[0], "This is a test.");
Assert.AreEqual(sents[1], "There are many tests, this is the second");
const string sampleSentences3 = "This is a \"test\". He said \"There are many tests, this is the second.\"";
sents = sentDetect.SentDetect(sampleSentences3);
Assert.AreEqual(sents.Length, 2);
probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(probs.Length, 2);
Assert.AreEqual(sents[0], "This is a \"test\".");
Assert.AreEqual(sents[1], "He said \"There are many tests, this is the second.\"");
const string sampleSentences4 = "This is a \"test\". I said \"This is a test.\" Any questions?";
sents = sentDetect.SentDetect(sampleSentences4);
Assert.AreEqual(sents.Length, 3);
probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(probs.Length, 3);
Assert.AreEqual(sents[0], "This is a \"test\".");
Assert.AreEqual(sents[1], "I said \"This is a test.\"");
Assert.AreEqual(sents[2], "Any questions?");
const string sampleSentences5 = "This is a one sentence test space at the end. ";
sents = sentDetect.SentDetect(sampleSentences5);
Assert.AreEqual(1, sentDetect.GetSentenceProbabilities().Length);
Assert.AreEqual(sents[0], "This is a one sentence test space at the end.");
const string sampleSentences6 = "This is a one sentences test with tab at the end. ";
sents = sentDetect.SentDetect(sampleSentences6);
Assert.AreEqual(sents[0], "This is a one sentences test with tab at the end.");
const string sampleSentences7 = "This is a test. With spaces between the two sentences.";
sents = sentDetect.SentDetect(sampleSentences7);
Assert.AreEqual(sents[0], "This is a test.");
Assert.AreEqual(sents[1], "With spaces between the two sentences.");
const string sampleSentences9 = "";
sents = sentDetect.SentDetect(sampleSentences9);
Assert.AreEqual(0, sents.Length);
const string sampleSentences10 = " "; // whitespaces and tabs
sents = sentDetect.SentDetect(sampleSentences10);
Assert.AreEqual(0, sents.Length);
const string sampleSentences11 = "This is test sentence without a dot at the end and spaces ";
sents = sentDetect.SentDetect(sampleSentences11);
Assert.AreEqual(sents[0], "This is test sentence without a dot at the end and spaces");
probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(1, probs.Length);
const string sampleSentence12 = " This is a test.";
sents = sentDetect.SentDetect(sampleSentence12);
Assert.AreEqual(sents[0], "This is a test.");
const string sampleSentence13 = " This is a test";
sents = sentDetect.SentDetect(sampleSentence13);
Assert.AreEqual(sents[0], "This is a test");
// Test that sentPosDetect also works
var pos = sentDetect.SentPosDetect(sampleSentences2);
Assert.AreEqual(pos.Length, 2);
probs = sentDetect.GetSentenceProbabilities();
Assert.AreEqual(probs.Length, 2);
Assert.AreEqual(new Span(0, 15), pos[0]);
Assert.AreEqual(new Span(16, 56), pos[1]);
}
public static string[] SplitSentences(string Text)
{
var modelStream = new java.io.ByteArrayInputStream(Resource.en_sent);
var model = new SentenceModel(modelStream);
var detector = new SentenceDetectorME(model);
return detector.sentDetect(Text);
}
