/// <summary>
/// Executes Sentiment and EntitiesMentioned analysis.
/// </summary>
public IOutcome<AnalysisResult> Analyze(StanfordCoreNLP pipeline, string text)
{
//Create annotated document
Annotation doc = new Annotation(text);
pipeline.annotate(doc);
//Validate
var sentences = doc.get(typeof(CoreAnnotations.SentencesAnnotation));
if (sentences == null)
return Outcomes.Outcomes
.Failure<AnalysisResult>()
.WithMessage("No sentences detected.");
//Analyze
var result = new AnalysisResult()
{
Sentiment = GetSentiment((ArrayList)sentences),
MentionedEntities = GetMentions(doc)
};
return Outcomes.Outcomes
.Success<AnalysisResult>()
.WithValue(result);
}
static void Main()
{
// Path to the folder with models extracted from `stanford-corenlp-3.9.1-models.jar`
var jarRoot = @"..\..\..\..\data\paket-files\nlp.stanford.edu\stanford-corenlp-full-2018-10-05\models";
// Text for processing
var text = "Kosgi Santosh sent an email to Stanford University. He didn't get a reply.";
// Annotation pipeline configuration
var props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, ner,dcoref");
props.setProperty("ner.useSUTime", "0");
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
// Annotation
var annotation = new Annotation(text);
pipeline.annotate(annotation);
// Result - Pretty Print
using (var stream = new ByteArrayOutputStream())
{
pipeline.prettyPrint(annotation, new PrintWriter(stream));
Console.WriteLine(stream.toString());
stream.close();
}
}
//-------------------------------------------------------------------------------------- Stanford Core NLP -----------------------------------------
//-- Better for Entity recognition
public static void buildPipeline(string text)
{//https://interviewbubble.com/getting-started-with-stanford-corenlp-a-stanford-corenlp-tutorial/
// Path to the folder with models extracted from `stanford-corenlp-3.7.0-models.jar`
var jarRoot = @"..\..\..\..\data\paket-files\nlp.stanford.edu\stanford-corenlp-full-2016-10-31\models";
// creates a StanfordCoreNLP object, with POS tagging, lemmatization,
// NER, parsing, and coreference resolution
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
// create an empty Annotation just with the given text
Annotation document = new Annotation(text);
// run all Annotators on this text
pipeline.annotate(document);
//Finished processing the document here
// Result - Pretty Print
using (var stream = new ByteArrayOutputStream())
{
pipeline.prettyPrint(document, new PrintWriter(stream));
Debug.WriteLine(stream.toString());
stream.close();
}
}
/// <summary>
/// Gets stem text
/// </summary>
/// <param name="text">Text to stem</param>
/// <returns>Text that is stemmed</returns>
public string GetStemmedText(string text)
{
try
{
// Annotation
var annotation = new Annotation(text);
_pipeLine.annotate(annotation);
// Sentence
ArrayList sentences = annotation.get(_sentencesAnnotation.getClass()) as ArrayList;
CoreMap sentence = sentences.get(0) as CoreMap;
// Token
ArrayList tokens = sentence.get(_tokensAnnotation.getClass()) as ArrayList;
CoreLabel token = tokens.get(0) as CoreLabel;
// Lemma
string lemma = token.get(_lemmaAnnotation.getClass()).ToString();
return(lemma);
}
catch (Exception)
{
return(null);
}
}
public void analyseText(string text, out float posScoreSum, out float negScoreSum)
{
float posScore, negScore;
var annotation = new Annotation(text);
thePipeline.annotate(annotation);
posScoreSum = 0;
negScoreSum = 0;
var sentences = annotation.get(typeof(CoreAnnotations.SentencesAnnotation));
foreach (Annotation sentence in sentences as ArrayList)
{
var tokens = sentence.get(typeof(CoreAnnotations.TokensAnnotation));
foreach (CoreLabel token in tokens as ArrayList)
{
string word = token.get(typeof(CoreAnnotations.TextAnnotation)).ToString();
string pos = token.get(typeof(CoreAnnotations.PartOfSpeechAnnotation)).ToString();
string wordLemma = token.get(typeof(CoreAnnotations.LemmaAnnotation)).ToString();
var basicPOS = getBasicPOS(pos);
if (basicPOS != null)
{
theSWNDictionary.GetScore(wordLemma, basicPOS, out posScore, out negScore);
posScoreSum += posScore;
negScoreSum += negScore;
}
}
}
}
static void Main()
{
// Path to the folder with models extracted from `stanford-corenlp-3.7.0-models.jar`
var jarRoot = @"..\..\..\..\paket-files\nlp.stanford.edu\stanford-corenlp-full-2016-10-31\models";
// Text for processing
var text = "Kosgi Santosh sent an email to Stanford University. He didn't get a reply.";
// Annotation pipeline configuration
var props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, parse, ner,dcoref");
props.setProperty("ner.useSUTime", "0");
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
// Annotation
var annotation = new Annotation(text);
pipeline.annotate(annotation);
// Result - Pretty Print
using (var stream = new ByteArrayOutputStream())
{
pipeline.prettyPrint(annotation, new PrintWriter(stream));
Console.WriteLine(stream.toString());
stream.close();
}
}
public Tvn.Cosine.Text.Nlp.Document Process(string text)
{
lock (syncLock)
{
var sentences = new List <Sentence>();
var tokens = new List <Token>();
var annotation = new Annotation(text);
pipeline.annotate(annotation);
var sentencesStanford = (java.util.List)annotation.get(typeof(CoreAnnotations.SentencesAnnotation));
if (sentencesStanford != null && sentencesStanford.size() > 0)
{
for (int i = 0; i < sentencesStanford.size(); ++i)
{
var sentence = (CoreMap)sentencesStanford.get(i);
var sentiment = (string)sentence.get(typeof(SentimentClass));
var tree = (Tree)sentence.get(typeof(SentimentAnnotatedTree));
var score = RNNCoreAnnotations.getPredictions(tree).getMatrix().getData();
var sentDic = new Dictionary <Sentiment, double>();
var tokensSentence = getTokens((java.util.List)sentence.get(typeof(CoreAnnotations.TokensAnnotation)));
var ner = getNamedEntities(tokensSentence);
for (uint s = 0; s < score.Length; ++s)
{
sentDic[new Sentiment(s, s.ToString())] = score[s];
}
sentences.Add(new Sentence(sentence.ToString(), tokensSentence, ner, new Sentiment(0, sentiment), sentDic));
tokens.AddRange(tokensSentence);
}
}
return(new Tvn.Cosine.Text.Nlp.Document(text, sentences, tokens));
}
}
public Annotation Annotate(string text)
{
var annotation = new Annotation(text);
_pipeline.annotate(annotation);
return(annotation);
}
public async Task <List <MCQuote> > GetQuotes(string data)
{
List <MCQuote> quotes = new List <MCQuote>();
try
{
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(_modelPath);
CoreDocument doc = new CoreDocument(data);
_pipeline.annotate(doc);
for (int i = 0; i < doc.quotes().size(); i++)
{
try
{
CoreQuote q = (CoreQuote)doc.quotes().get(i);
quotes.Add(new MCQuote(q));
}
catch (Exception e)
{ }
}
Directory.SetCurrentDirectory(curDir);
}
catch (Exception e)
{
throw new SpanishCoreNLPQuoteException(e.Message, e);
}
return(quotes);
}
/*Stem the given word with, return the stemmed word
*/
public List <string> Stem(string word)
{
if (pipeline == null)
{
Initial();
}
var lemmas = new List <String>();
// create an empty Annotation just with the given text
var document = new Annotation(word);
// run all Annotators on this text
try
{
pipeline.annotate(document);
}
catch (Exception)
{
return(null);
}
// Iterate over all of the sentences found
var senObj = new edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation();
var obj = document.get(senObj.getClass());
var tokenObj = new edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation();
var lemmaObj = new edu.stanford.nlp.ling.CoreAnnotations.LemmaAnnotation();
var sentences = (ArrayList)obj;
foreach (CoreMap sentence in sentences)
{
// Iterate over all tokens in a sentence
lemmas.AddRange(from CoreLabel token in (ArrayList)sentence.get(tokenObj.getClass())
select(string) token.get(lemmaObj.getClass()));
}
return(lemmas);
}
/// <summary>
/// Accepts a document (text) file, lets CoreNLP process (annotate) it
/// and returns an adapter that allows iterating the output
///
/// also, the text file (normalized) is returned
/// </summary>
/// <param name="file"></param>
/// <returns>returns null if fails</returns>
public StanfordDocumentFacade Annotate(FileInfo file)
{
if (!this.Initialized)
{
return(null);
}
try {
string data;
NormalizeFile(file, out data);
var annotation = new edu.stanford.nlp.pipeline.Annotation(data);
_pipeLine.annotate(annotation);
return(new StanfordDocumentFacade(annotation, data));
} catch (Exception ex) {
throw ex;
}
}
public static JObject ProcessText(string text)
{
var annotation = new Annotation(text);
using (java.io.StringWriter writer = new java.io.StringWriter())
{
pipeline.annotate(annotation);
pipeline.jsonPrint(annotation, writer);
return(JObject.Parse(writer.toString()));
}
}
public static ProcessedEntity Classify(this StanfordCoreNLP nlp, string source)
{
CoreDocument document = new CoreDocument(source);
nlp.annotate(document);
return(document.sentences()
.toArray()
.OfType <CoreSentence>()
.Select(s => new ParsedSentence(s))
.Aggregate(new ProcessedEntity(), (r, s) => ProcessedEntity.Union(r, s.ToProcessedEntity())));
}
public string[] BreakIntoWords(string paragraph)
{
// create an empty Annotation just with the given text
Annotation document = new Annotation(paragraph);
// run all Annotators on this text
_pipline.annotate(document);
// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
object obj = document.get(new CoreAnnotations.SentencesAnnotation().getClass());
ArrayList sentences = obj as ArrayList;
List <string> words = new List <string>(sentences.size() * 10); // Guess how many words per statement
int i = 0;
foreach (CoreMap sentence in sentences)
{
words.AddRange(_wordBreaker.BreakParagraph(sentence.ToString()));
}
return(words.ToArray());
}
public List <string> TokenizeAndLemmatize(string documentText)
{
var annotation = new Annotation(documentText);
_pipeline.annotate(annotation);
var ret = new List <string>();
var tokenKey = ClassLiteral <CoreAnnotations.TokensAnnotation> .Value;
var lemmaKey = ClassLiteral <CoreAnnotations.LemmaAnnotation> .Value;
var tokenItems = annotation.get(tokenKey) as ArrayList;
if (tokenItems == null)
{
return(ret);
}
ret.AddRange(tokenItems.OfType <CoreLabel>().Select(tmp => (string)tmp.get(lemmaKey)));
return(ret);
}
public void Analysis(string path)
{
var document = this.ReadAnnotation(path);
Properties props = new Properties();
//props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse,relation,,mention,coref");
//var modelsDirectory = jarRoot ;
// Loading POS Tagger
//String modPath = @"D:\Tesis2016\Jarvis\Lincoln\Models\";
//props.put("pos.model", modPath + @"pos-tagger\english-bidirectional-distsim.tagger");
//props.put("ner.model", modPath + "ner/english.all.3class.distsim.crf.ser.gz");
//props.put("parse.model", modPath + "lexparser/englishPCFG.ser.gz");
//props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
//props.put("sutime.binders", "0");
//props.put("sutime.rules", modPath + "sutime/defs.sutime.txt, " + modPath + "sutime/english.sutime.txt");
//props.put("dcoref.demonym", modPath + "dcoref/demonyms.txt");
//props.put("dcoref.states", modPath + "dcoref/state-abbreviations.txt");
//props.put("dcoref.animate", modPath + "dcoref/animate.unigrams.txt");
//props.put("dcoref.inanimate", modPath + "dcoref/inanimate.unigrams.txt");
//props.put("dcoref.big.gender.number", modPath + "dcoref/gender.data.gz");
//props.put("dcoref.countries", modPath + "dcoref/countries");
//props.put("dcoref.states.provinces", modPath + "dcoref/statesandprovinces");
//props.put("dcoref.singleton.model", modPath + "dcoref/singleton.predictor.ser");
//props.put("ner.useSUTime", "0");
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
props.setProperty("sutime.binders", "0");
props.setProperty("ner.useSUTime", "false");
var jarRoot = @"D:\Tesis2016\Jarvis\Lincoln\Models";
var curDir = Environment.CurrentDirectory;
System.IO.Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
pipeline.annotate(document);
System.IO.Directory.SetCurrentDirectory(curDir);
FileOutputStream os = new FileOutputStream(new File("coreference_output.xml"));
pipeline.xmlPrint(document, os);
}
private Annotation PrepareAnnotation()
{
var props = new Properties();
props.put("pos.model", modelsDir + "pos-tagger/english-left3words/english-left3words-distsim.tagger");
props.put("ner.model", modelsDir + "ner/english.conll.4class.distsim.crf.ser.gz");
props.put("parse.model", modelsDir + "lexparser/englishPCFG.ser.gz");
props.put("sutime.rules", modelsDir + "sutime/defs.sutime.txt, " + modelsDir + "sutime/english.sutime.txt");
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse");
props.setProperty("sutime.binders", "0");
props.setProperty("ner.useSUTime", "0");
var pipeline = new StanfordCoreNLP(props);
var annotatedText = new Annotation(text);
pipeline.annotate(annotatedText);
return annotatedText;
}
public int SentiAnalysis(string text)
{
// Annotation
var annotation = new edu.stanford.nlp.pipeline.Annotation(text);
pipeline.annotate(annotation);
using (var stream = new ByteArrayOutputStream())
{
pipeline.prettyPrint(annotation, new PrintWriter(stream));
int mainSentiment = 0;
int longest = 0;
String[] sentimentText = { "Very Negative", "Negative", "Neutral", "Positive", "Very Positive" };
NumberFormat NF = new DecimalFormat("0.0000");
var sentences = annotation.get(new CoreAnnotations.SentencesAnnotation().getClass()) as ArrayList;
foreach (CoreMap sentence in sentences)
{
Tree tree = (Tree)sentence.get(typeof(SentimentCoreAnnotations.SentimentAnnotatedTree));
int sentiment = edu.stanford.nlp.neural.rnn.RNNCoreAnnotations.getPredictedClass(tree);
String partText = sentence.ToString();
try
{
}
catch (IndexOutOfRangeException e)
{
}
if (partText.Length > longest)
{
mainSentiment = sentiment;
longest = partText.Length;
}
return(sentiment);
}
}
return(-1);
}
private Annotation PrepareAnnotation()
{
var props = new Properties();
props.put("pos.model", modelsDir + "pos-tagger/english-left3words/english-left3words-distsim.tagger");
props.put("ner.model", modelsDir + "ner/english.conll.4class.distsim.crf.ser.gz");
props.put("parse.model", modelsDir + "lexparser/englishPCFG.ser.gz");
props.put("sutime.rules", modelsDir + "sutime/defs.sutime.txt, " + modelsDir + "sutime/english.sutime.txt");
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse");
props.setProperty("sutime.binders", "0");
props.setProperty("ner.useSUTime", "0");
var pipeline = new StanfordCoreNLP(props);
var annotatedText = new Annotation(text);
pipeline.annotate(annotatedText);
return(annotatedText);
}
public void Parse(string sentence)
{
if (pipeline == null)
{
Initial();
}
Annotation context = new Annotation(sentence);
pipeline.annotate(context);
this.tokens = (ArrayList)context.get(tokenObj.getClass());
var sentences = (ArrayList)context.get(senObj.getClass());
foreach (CoreMap sen in sentences)
{
this.dependencies = (SemanticGraph)sen.get(depObj.getClass());
break;
}
}
public List <string> Tokenize(string sequence)
{
if (sequence == null)
{
throw new Exception("Sequence should not be null for tokenizer.");
}
if (pipeline == null)
{
Initial();
}
var document = new Annotation(sequence);
pipeline.annotate(document);
var tokens = (ArrayList)document.get(tokenObj.getClass());
return((from CoreMap token in tokens select token.ToString()).ToList());
}
private NlpAnalysis AnalyseText(NlpQuery query)
{
var analysis = new NlpAnalysis();
if (IsInitialized)
{
var text = query.Text;
var annotation = new Annotation(text);
m_pipeline.annotate(annotation);
var sentences = annotation.get(typeof(CoreAnnotations.SentencesAnnotation)) as ArrayList;
if (sentences != null)
{
analysis.Sentences = sentences;
}
}
return(analysis);
}
// Sample from https://stanfordnlp.github.io/CoreNLP/coref.html
static void Main()
{
var jarRoot = @"..\..\..\..\data\paket-files\nlp.stanford.edu\stanford-corenlp-full-2018-02-27\models";
Annotation document = new Annotation("Barack Obama was born in Hawaii. He is the president. Obama was elected in 2008.");
Properties props = new Properties();
props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse,mention,coref");
props.setProperty("ner.useSUTime", "0");
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
pipeline.annotate(document);
var corefChainAnnotation = new CorefCoreAnnotations.CorefChainAnnotation().getClass();
var sentencesAnnotation = new CoreAnnotations.SentencesAnnotation().getClass();
var corefMentionsAnnotation = new CorefCoreAnnotations.CorefMentionsAnnotation().getClass();
Console.WriteLine("---");
Console.WriteLine("coref chains");
var corefChain = document.get(corefChainAnnotation) as Map;
foreach (CorefChain cc in corefChain.values().toArray())
{
Console.WriteLine($"\t{cc}");
}
var sentences = document.get(sentencesAnnotation) as ArrayList;
foreach (CoreMap sentence in sentences.toArray())
{
Console.WriteLine("---");
Console.WriteLine("mentions");
var corefMentions = sentence.get(corefMentionsAnnotation) as ArrayList;
foreach (Mention m in corefMentions)
{
Console.WriteLine("\t" + m);
}
}
}
public static IEnumerable <POS> Extract(string text, ref NLPCount count)
{
var segment = new List <POS>();
if (string.IsNullOrEmpty(text))
{
return(segment);
}
var document = new Annotation(text);
pipeline.annotate(document);
var sentencesAnnotation = new SentencesAnnotation();
var tokensAnnotation = new TokensAnnotation();
var textAnnotation = new TextAnnotation();
var partOfSpeechAnnotation = new PartOfSpeechAnnotation();
java.util.ArrayList sentenceArrayList = (java.util.ArrayList)document.get(sentencesAnnotation.getClass());
var sentences = sentenceArrayList.toArray();
count.SentenceCount += sentences.Length;
for (int i = 0; i < sentences.Length; i++)
{
var sentence = (edu.stanford.nlp.util.CoreMap)sentences[i];
var tokenArray = ((java.util.ArrayList)sentence.get(tokensAnnotation.getClass()));
var tokens = tokenArray.toArray();
count.WordsPhraseCount += tokens.Length;
for (int j = 0; j < tokens.Length; j++)
{
var coreLabel = (edu.stanford.nlp.ling.CoreLabel)tokens[j];
string posTag = (string)coreLabel.get(partOfSpeechAnnotation.getClass());
string word = (string)coreLabel.get(textAnnotation.getClass());
if (word.Length <= 100)
{
segment.Add(new POS()
{
Text = word, PosTag = posTag
});
}
}
}
return(segment.ToList());
}
public Emotion GetEmotion(string text)
{
var annotation = new Annotation(text);
stanfordNLP.annotate(annotation);
return
((annotation.get(sentencesAnnotationClassName) as ArrayList)
.toArray().Select(ParseEmotion).FirstOrDefault());
Emotion ParseEmotion(object s)
{
var sentence = s as Annotation;
var sentenceTree = sentence.get(emotionAnnotationTreeClassName) as Tree;
var emotion = RNNCoreAnnotations.getPredictedClass(sentenceTree);
return(new Emotion(emotion));
}
}
public static IEnumerable <string> FindWords(string text)
{
// Path to the folder with models extracted from `stanford-corenlp-3.4-models.jar`
var jarRoot = @"stanford-corenlp-3.4-models\";
// Annotation pipeline configuration
var props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
props.setProperty("sutime.binders", "0");
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
// Annotation
var annotation = new Annotation(text);
pipeline.annotate(annotation);
// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
var sentences = annotation.get(typeof(CoreAnnotations.SentencesAnnotation));
if (sentences == null)
{
return(Enumerable.Empty <string>());
}
var words = new List <string>();
foreach (Annotation sentence in sentences as ArrayList)
{
words.Add(sentence.toString());
}
return(words);
}
public void CorefTest()
{
Annotation document = new Annotation("Barack Obama was born in Hawaii. He is the president. Obama was elected in 2008.");
Properties props = new Properties();
props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse,coref");
props.setProperty("ner.useSUTime", "false");
var curDir = Environment.CurrentDirectory;
Directory.SetCurrentDirectory(Files.CoreNlp.JarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
pipeline.annotate(document);
var corefChainAnnotation = new CorefCoreAnnotations.CorefChainAnnotation().getClass();
var sentencesAnnotation = new CoreAnnotations.SentencesAnnotation().getClass();
var corefMentionsAnnotation = new CorefCoreAnnotations.CorefMentionsAnnotation().getClass();
Console.WriteLine("---");
Console.WriteLine("coref chains");
var corefChain = (Map)document.get(corefChainAnnotation);
foreach (CorefChain cc in corefChain.values().toArray())
{
Console.WriteLine($"\t{cc}");
}
var sentences = (ArrayList)document.get(sentencesAnnotation);
foreach (CoreMap sentence in sentences.toArray())
{
Console.WriteLine("---");
Console.WriteLine("mentions");
var corefMentions = (ArrayList)sentence.get(corefMentionsAnnotation);
foreach (Mention m in corefMentions)
{
Console.WriteLine("\t" + m);
}
}
}
public static string findSentiment(String line)
{
// // Path to models extracted from `stanford-parser-3.5.1-models.jar`
// var jarRoot = @"..\..\..\..\paket-files\nlp.stanford.edu\stanford-parser-full-2015-01-30\models\";
// var modelsDirectory = jarRoot + @"\edu\stanford\nlp\models";
//var modelsDirectory = @"\edu\stanford\nlp\models";
var jarRoot = @"..\..\src\stanford-corenlp\";
//var modelsDirectory = jarRoot + @"\edu\stanford\nlp\models";
// Loading english PCFG parser from file
//var lp = LexicalizedParser.loadModel(modelsDirectory + @"\lexparser\englishPCFG.ser.gz");
//try
{
Properties prop = new Properties();
prop.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
var curDir = Environment.CurrentDirectory;
System.IO.Directory.SetCurrentDirectory(jarRoot);
StanfordCoreNLP pipeline = new StanfordCoreNLP(prop);
System.IO.Directory.SetCurrentDirectory(curDir);
String[] polarity = { "Very Negative", "Negative", "Neutral", "Positive", "Very Positive" };
int score = 0;
if ((line != null) && (line.Length > 0))
{
Annotation annotation = new Annotation(line);
pipeline.annotate(annotation);
foreach (CoreMap sent in (dynamic)annotation.get(new CoreAnnotations.SentencesAnnotation().getClass()))
{
Tree tree = (Tree)sent.get(new SentimentCoreAnnotations.AnnotatedTree().getClass());
score = RNNCoreAnnotations.getPredictedClass(tree);
//Console.WriteLine("The polarity of the satement is "+polarity[score]);
}
}
return(polarity[score]);
}
//catch { return "nothing"; }
}
public void StanfordCoreNLPForChinese()
{
// Text for processing
var text = "王尼玛跑的很快.";
// Annotation pipeline configuration
//var props = new Properties();
//props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
//props.setProperty("sutime.binders", "0");
//
string props = "StanfordCoreNLP-chinese.properties";
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
var pipeline = new StanfordCoreNLP(props);
// Annotation
var annotation = new Annotation(text);
pipeline.annotate(annotation);
var sentences = annotation.get(typeof(CoreAnnotations.SentencesAnnotation));
}
public static String Parse_Data(String sent, StanfordCoreNLP pipeline1)
{// extract meaningful words from user query
// Text for processing
var text = sent;
// Annotation
var annotation = new edu.stanford.nlp.pipeline.Annotation(text);
pipeline1.annotate(annotation);
// Result - Pretty Print
string output;
using (var stream = new ByteArrayOutputStream())
{
pipeline1.prettyPrint(annotation, new PrintWriter(stream));
System.Console.WriteLine(" it's stanford time ");
output = stream.toString();
stream.close();
}
return(output);
}
public static AnnotationObject Annotate(string content)
{
// Annotation
var annotation = new Annotation(content);
Pipeline.annotate(annotation);
// Result - Print
using var stream = new ByteArrayOutputStream();
Pipeline.jsonPrint(annotation, new PrintWriter(stream));
//-----
string serialized = stream.toString().Replace("\n", "");
var deserialized = Newtonsoft.Json.JsonConvert.DeserializeObject <AnnotationObject>(serialized);
//-----
stream.close();
return(deserialized);
}
public NlpResult DeserializeInput(StanfordCoreNLP pipeline, NlpResult nlpResult, string stringInput)
{
// Annotation
var annotation = new Annotation(stringInput);
pipeline.annotate(annotation);
// Result - Pretty Print
using (var stream = new ByteArrayOutputStream())
{
pipeline.jsonPrint(annotation, new PrintWriter(stream));
_jsonContentProvider.PopulateFromString(nlpResult, stream.toString());
Debug.WriteLine(stream.toString());
stream.close();
}
return(nlpResult);
}
private List <string> Split(string sentence2)
{
var jarRoot = "stanford-corenlp-full-2018-10-05/stanford-corenlp-3.9.2-models";
// Annotation pipeline configuration
var props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
props.setProperty("sutime.binders", "0");
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
System.Console.WriteLine(curDir);
Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
Directory.SetCurrentDirectory(curDir);
// Annotation
var annotation = new Annotation(sentence2);
pipeline.annotate(annotation);
// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
var sentences = annotation.get(typeof(CoreAnnotations.SentencesAnnotation));
if (sentences == null)
{
return(null);;
}
foreach (Annotation sentence in sentences as ArrayList)
{
System.Console.WriteLine(sentence);
}
return(null);
}
//使用nlp將文章分析後回傳key
private List<string> nlp(string sentence)
{
List<string> return_key = new List<string>();
string Relay_file = ".\\xml";
string Relay_name = "Relay.xml";
string Relay_path = Relay_file+ "\\" + Relay_name;
// Path to the folder with models extracted from `stanford-corenlp-3.4-models.jar`
var jarRoot = @"stanford-corenlp-3.5.2-models\";
// Annotation pipeline configuration
var props = new java.util.Properties();
props.setProperty("ner.useSUTime", "false");
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
props.setProperty("sutime.binders", "0");
// We should change current directory, so StanfordCoreNLP could find all the model files automatically
var curDir = Environment.CurrentDirectory;
System.IO.Directory.SetCurrentDirectory(jarRoot);
var pipeline = new StanfordCoreNLP(props);
System.IO.Directory.SetCurrentDirectory(curDir);
// Annotation
var annotation = new Annotation(sentence);
pipeline.annotate(annotation);
//輸出nlp分析結果至Relay.xml
FileOutputStream os = new FileOutputStream(new File(Relay_file, Relay_name));
pipeline.xmlPrint(annotation, os);
os.close();
//呼叫ner將單字組合為有意義的key組裝
foreach(string k in ner(Relay_path))
{
return_key.Add(k);
}
return return_key;
}
