/// <summary>
/// Setup extended tagger that includes POS, lemma and entity analysis
/// </summary>
private void SetupExtendedTagger()
{
PerformanceTester.StartMET("NLP");
// Get path to Stanford NLP models
var jarRoot = Path.Combine(Utility.GetResourcesFolder(), @"stanford-corenlp-3.9.2-models");
// Turn off logging
RedwoodConfiguration.current().clear().apply();
var props = new java.util.Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner");
// Makes Named Entity Recognition work in the library
props.setProperty("ner.useSUTime", "0");
props.put("ner.applyFineGrained", "0");
props.put("ner.fine.regexner.mapping", jarRoot + @"\edu\stanford\nlp\models\kbp\english\");
// Set current directory
var curDir = Environment.CurrentDirectory;
var modelsDirectory = curDir + "\\" + jarRoot + @"\edu\stanford\nlp\models";
Directory.SetCurrentDirectory(jarRoot);
// Load Stanford NLP
Tagger = new StanfordCoreNLP(props);
PerformanceTester.StopMET("NLP");
}
/// <summary>
/// Get sentences from the given message text
/// </summary>
/// <param name="text">The message text</param>
/// <returns>A list with the sentences</returns>
public List <object> GetSentences(string text)
{
PerformanceTester.StartMET("GetSentences");
var annotation = new Annotation(text);
Tagger.annotate(annotation);
Sentences = annotation.get(new CoreAnnotations.SentencesAnnotation().getClass()) as ArrayList;
PerformanceTester.StopMET("GetSentences");
return(new List <object>(Sentences.toArray()));
}
/// <summary>
/// Tag the given message text
/// </summary>
/// <param name="text">The message text</param>
/// <returns>A list with the tagged words</returns>
public List <TaggedWord> Tag(string text, bool useSavedSentences = false)
{
PerformanceTester.StartMET("Tagging");
var list = new List <TaggedWord>();
ArrayList sentences = null;
if (useSavedSentences && Sentences != null)
{
sentences = Sentences;
}
else
{
var annotation = new Annotation(text);
Tagger.annotate(annotation);
sentences = annotation.get(new CoreAnnotations.SentencesAnnotation().getClass()) as ArrayList;
}
foreach (CoreMap sentence in sentences)
{
var tokens = sentence.get(new
CoreAnnotations.TokensAnnotation().getClass()) as ArrayList;
foreach (CoreLabel token in tokens)
{
var original = token.get(new CoreAnnotations.OriginalTextAnnotation().getClass());
var after = token.get(new CoreAnnotations.AfterAnnotation().getClass());
var before = token.get(new CoreAnnotations.BeforeAnnotation().getClass());
var word = token.get(new CoreAnnotations.TextAnnotation().getClass());
var pos = token.get(new CoreAnnotations.PartOfSpeechAnnotation().getClass());
//var ner = token.get(new CoreAnnotations.NamedEntityTagAnnotation().getClass());
//var lemma = token.get(new CoreAnnotations.LemmaAnnotation().getClass());
var taggedWord = new TaggedWord()
{
Word = word.ToString(),
Original = original.ToString(),
WhiteSpaceCharacterAfter = after.ToString(),
WhiteSpaceCharacterBefore = before.ToString(),
POSStringIdentifier = pos.ToString(),
//Lemma = lemma.ToString(),
//NERStringIdentifier = ner.ToString()
};
list.Add(taggedWord);
}
}
PerformanceTester.StopMET("Tagging");
return(list);
}
//*************************************************/
// METHODS
//*************************************************/
#region Methods
/// <summary>
/// Setup tagger including POS
/// </summary>
private void SetupTagger()
{
PerformanceTester.StartMET("NLP");
// Get path to Stanford NLP models
var jarRoot = Path.Combine(Utility.GetResourcesFolder(), @"stanford-corenlp-3.9.2-models");
// Turn off logging
RedwoodConfiguration.current().clear().apply();
// Set properties
var props = new java.util.Properties();
props.setProperty("annotators", "tokenize, ssplit, pos");
// Set current directory
var curDir = Environment.CurrentDirectory;
var modelsDirectory = curDir + "\\" + jarRoot + @"\edu\stanford\nlp\models";
Directory.SetCurrentDirectory(jarRoot);
// Load Stanford NLP
Tagger = new StanfordCoreNLP(props);
PerformanceTester.StopMET("NLP");
}
/// <summary>
/// Analyze a text message from the user
/// </summary>
/// <param name="text"></param>
/// <returns>A message result</returns>
private MessageResult AnalyzeText(string text)
{
try
{
var tempText = text.ToLower();
// Get sentences that has been written
var sentences = NLPHelper.GetSentences(tempText);
// Does not support more than one sentence
if (sentences.Count > 1)
{
return(new MessageResult(Properties.Resources.i_cannot_process_more_than_one_sentence));
}
// Tagging on the given words
List <TaggedWord> wordList = null;
// Function for tagging
Action <bool> tagging = (useSavedSentences) =>
{
// Insert a question mark at the end of a text
if (!tempText.EndsWith("?"))
{
tempText += "?";
}
// Tag the words in the text
wordList = NLPHelper.Tag(tempText, useSavedSentences);
};
PerformanceTester.StartMET("Use calculator");
// If text has any numbers use the calculator
string output;
if (SimpleCalculatorHelper.CheckInput(ref tempText, out output))
{
// If text is not math command
if (output == null)
{
var input = tempText;
tagging(false);
if (wordList.Any(x => x.IsWHWord || x.IsVerb))
{
// Skip the WH-words and verbs in the beginning when using calculator
var context = wordList
.SkipWhile(x => x.IsWHWord || x.IsVerb)
.Where(x => x.POSStringIdentifier != ".")
.ToList();
// Get text
var joined = context.ListToString();
// If sentence starts with a WH word insert an equal sign
if (wordList.FirstOrDefault().IsWHWord)
{
input = joined.Insert(0, "=");
}
}
// Use calculator
output = SimpleCalculatorHelper.UseCalculator(input);
PerformanceTester.StopMET("Use calculator");
}
// If output is not null write the calculator result to the user
if (output != null)
{
return(new MessageResult()
{
Messages = new MessageObject[] { new MessageObject(output) }
});
}
}
PerformanceTester.StopMET("Use calculator");
tagging(true);
PerformanceTester.StartMET("Analyze word list");
// Analyze the input
var analyzeResult = AnalyzeWordList(wordList);
PerformanceTester.StopMET("Analyze word list");
// If success
if (analyzeResult.IsSuccess)
{
// Analyze the search strings
return(AnalyzeSearchStrings(analyzeResult.SearchStrings));
}
// If error
else
{
return(new MessageResult(analyzeResult.ErrorMessage));
}
}
catch (Exception mes)
{
return(new MessageResult(mes.Message));
}
}
/// <summary>
/// Analyze a given list of search strings
/// </summary>
/// <param name="list">The list with the search strings</param>
/// <returns>A message result</returns>
private MessageResult AnalyzeSearchStrings(List <string> list)
{
List <MessageObject> messages = new List <MessageObject>();
List <Material> materials = null;
try
{
PerformanceTester.StartMET("Analyze search strings");
foreach (var str in list)
{
// Checking for bot command
if (RunCommand(str))
{
return(new MessageResult());
}
// Search for term and topic
Term term = null; Topic topic = null;
Entity.GetTermAndTopic(out term, out topic, str);
if (topic == null && term == null)
{
continue;
}
// Get materials
// A term and a topic has the same name
if (term != null && topic != null)
{
PerformanceTester.StopMET("Analyze search strings");
messages.Add(new MessageObject(term, topic));
return(new MessageResult(messages.ToArray()));
}
// Get materials for a topic
else if (topic != null)
{
materials = Entity.GetMaterials(topicId: topic.Id);
}
// Get materials for a term
else
{
materials = Entity.GetMaterials(term.Id);
}
PerformanceTester.StopMET("Analyze search strings");
if (materials.Count == 0)
{
return(new MessageResult(Properties.Resources.no_materials_found));
}
// Add bot message
messages.Add(new MessageObject(string.Format(Properties.Resources.this_is_what_i_found_about, term != null ? term.Name.ToLower() : topic.Name.ToLower())));
// Add materials
foreach (var material in materials)
{
messages.Add(new MessageObject(material));
}
return(new MessageResult(messages.ToArray()));
}
PerformanceTester.StopMET("Analyze search strings");
// One noun
if (list.Count == 1)
{
return(new MessageResult(string.Format(Properties.Resources.i_do_not_know_anything_about_that_noun, list[0], GetTopics())));
}
// More nouns
else
{
return(new MessageResult(string.Format(Properties.Resources.i_do_not_know_anything_about_those_nouns, GetTopics())));
}
}
catch (Exception mes)
{
return(new MessageResult(mes.Message));
}
}
