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File: Docs/doc3.txt
Role: Documentation
Content type: text/plain
Description: Documentation
Class: PHP Sentence Tokenizer
Parse sentences and extract their word features
Author: By
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Date: 2 months ago
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Remembering / Forgeting

 " Forgeting is important to keep things relevant and fresh.  It allows to speed out the process and save memory
 
 Memory = Word Bank
 
 Different levels of memory ->
	* Fresh Memory ( quick access memory )
	* Permanent Memory ( Hard to forget )
	* Temporary Memory ( Cash memory, it needs more re-inforcement to become permanent )
	* Bare Memory	   ( Incomplete, briefly touched; it may dissapear at any moment if not confirmed )
	* Forgotten: dumped( Is there, but it won't retrive unless a significan part is re-activated or re-learned );
	
 Frequency: Level of frequency depends on the number of times a word has been detected within a period of time.  
	    The greatest the frequency, the easier to retrive the word and higher chances to remain on permanent memory.
	    
	  Every word needs a word id and frequency.  The application needs a time listener to determine the frequency.
	  
 Freshness:  The time past since the word was last detected.  Every word is kept at the top of "Fresh Memory" everytime is detected.
	    When a new one is detected, the word past to the second placement in priority or freshness.
		      * Divide total list by 4 ( 4 stages of memory)
			result is the number of placements within each memory
	      placements = Num_items / 4  
	      FRESHNESS = 
	    
 Lenght:    Shorter words are easily retained in the Permanent memory than long words.  Long words need more exposition in order to be
	    remembered.
	    
	    Defined by the number of characters. 
	    
		(int) L = NUM_OF_CHARACTERS * (-1)		// To keep the number low and favor less over more.
	    
 Intensity: Words matching with objectives ( Grade System ) are more relevant to purpose, and therefore more desirables.  The have 
	   presedence over non matching words.
	   
	   * Relevance shows intensity: Intensity could be either positive or negative.  In this case, shallow takes the back seat.
	   * Hight intensity words will be remembered either positive or negative.  This will change the personality of the Bot.  
	   * The more possitive feedback, the nicer Bot we will have, the more negative, the most evil the Bot.
		For the purpose of WORD_RECORDING_VALUE, Relevance turns into a positive number;
	   
	   
	   WORD_RECORDING_VALUE = FREQUENCY + FRESHNESS + LENGHT + INTENSITY
	   
	   
	   
    
   
   
  
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