nlp-toolkit

id	nlp-toolkit
version	1.0.0
name	NLP Toolkit
description	Advanced NLP with perplexity scoring, burstiness analysis, and entropy calculation
author	NeoClaw Team
category	detection
tags	["nlp","perplexity","burstiness","entropy"]
dependencies	[]

NLP Toolkit

Advanced NLP analysis for AI content detection using statistical measures.

Implementation

/**
 * Analyze text using NLP metrics
 *  {}  - Text to analyze
 *  {}  - Configuration options
 *  {} NLP analysis results
 */
  () {
   {
    perplexityThreshold = ,
    burstinessThreshold = ,
    minTextLength = 
  } = options;

   (text. < minTextLength) {
     {
      : ,
      : minTextLength
    };
  }

  
   perplexity = (text);

  
   burstiness = (text);

  
   entropy = (text);

  
   tokenStats = (text);

  
   isAI = perplexity < perplexityThreshold && burstiness < burstinessThreshold;
   confidence = (perplexity, burstiness, entropy);

   {
    isAI,
    : .(confidence * ),
    : {
      : .(perplexity * ) / ,
      : .(burstiness * ) / ,
      : .(entropy * ) / 
    },
    tokenStats,
    : {
      : perplexityThreshold,
      : burstinessThreshold
    },
    : isAI ? 
       :
      
  };
}


 () {
   words = text.().();
   uniqueWords =  (words);
  
  
  
   ratio = uniqueWords. / words.;
   perplexity =  / ratio; 
  
   .(perplexity, );
}


 () {
   sentences = text.().( s.());
   (sentences. < )  ;

   lengths = sentences.( s.().);
   avg = lengths.( a + b, ) / lengths.;
   variance = lengths.( sum + .(len - avg, ), ) / lengths.;
   stdDev = .(variance);

  
   burstiness = stdDev / avg;

   .(burstiness, );
}


 () {
   chars = text.().();
   freq = {};

  
   ( char  chars) {
    freq[char] = (freq[char] || ) + ;
  }

  
   entropy = ;
   total = chars.;

   ( count  .(freq)) {
     p = count / total;
    entropy -= p * .(p);
  }

   entropy;
}


 () {
   words = text.().();
   uniqueWords =  (words);

   {
    : words.,
    : uniqueWords.,
    : .((uniqueWords. / words.) * ) / 
  };
}


 () {
  
   perplexityScore = .(,  - (perplexity / ));
  
  
   burstinessScore = .(,  - (burstiness / ));
  
  
   entropyScore = (entropy >  && entropy < ) ?  : ;

   confidence = (perplexityScore + burstinessScore + entropyScore) / ;
   .(confidence, );
}


. = {
  analyzeText,
  calculatePerplexity,
  calculateBurstiness,
  calculateEntropy
};