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AIMA-Java Core Algorithms from the book Artificial Intelligence a Modern Approach 3rd Ed.
There is a newer version: 3.0.0
package aima.core.nlp.parsing;

import java.util.ArrayList;
import java.util.List;

import aima.core.nlp.parsing.grammars.ProbCNFGrammar;
import aima.core.nlp.parsing.grammars.ProbUnrestrictedGrammar;
import aima.core.nlp.parsing.grammars.Rule;

/**
 * Artificial Intelligence A Modern Approach (3rd Edition): page 894.

 * 

 * 
 *  * function CYK-PARSE(words, grammar) returns P, a table of probabilities
 *   N <- LENGTH(words)
 *   M <- the number of nonterminal symbols in grammar
 *   P <- an array of size[M,N,N], initially all 0
 *   /* Insert Lexical rules for each word *\
 *   for i = 1 to N do
 *   	for each rule of form( X -> words_i[p]) do
 *   		P[X,i,1] <- p
 *   /* Combine first and second parts of right-hand sides of rules, from short to long *\
 *   for length = 2 to N do
 *   	for start = 1 to N - length + 1 do
 *   		for len1 = 1 to N -1 do 
 *   			len2 <- length - len1
 *   		for each rule of the form( X -> Y Z [p] ) do
 *   			p[X, start, length] <- MAX(P[X, start, length],
 *   						P[Y, start, len1] * P[Z, start + len1, len2] * p)
 *   return P
 * 
 * 
 * Figure 23.5 The CYK algorithm for parsing. Given a sequence of words,
 * it finds the most probable derivation for the whole sequence and for
 * each subsequence. It returns the whole table, P, in which an entry 
 * P[X, start, len] is the probability of the most probable X of length
 * len starting at position start. If there is no X of that size at that
 * location, the probability is 0.

 * 

 * 
 * @author Jonathon Belotti (thundergolfer)
 *
 */
public class CYK {
	
	public float[][][] parse( List words, ProbCNFGrammar grammar ) {
		final int N = length(words);
		final int M = grammar.vars.size(); 
		float[][][] P = new float[M][N][N]; // initialised to 0.0
		for( int i=0; i < N; i++ ) {
		   //for each rule of form( X -> words_i[p]) do
			//   P[X,i,1] <- p
			for( int j=0; j < grammar.rules.size(); j++ ) {
				Rule r = (Rule) grammar.rules.get(j);
				if( r.derives(words.get(i))) { 				 	// rule is of form X -> w, where w = words[i]
					int x = grammar.vars.indexOf(r.lhs.get(0)); // get the index of rule's LHS variable
					P[x][i][0] = r.PROB; 						// not P[X][i][1] because we use 0-based indexing
				}
			}
		}
		for( int length=2; length <= N; length++ ) {
			for( int start=1; start <= N - length + 1; start++ ) {
				for( int len1=1; len1 <= length -1; len1++ ) { // N.B. the book incorrectly has N-1 instead of length-1
					int len2 = length - len1;
					// for each rule of the form X -> Y Z, where Y,Z are variables of the grammar
					for( int j=0; j < grammar.rules.size(); j++ ) {
						Rule r = grammar.rules.get(j);
						if( r.rhs.size() == 2 ) {
							// get index of rule's variables X, Y, and Z
							int x = grammar.vars.indexOf(r.lhs.get(0));
							int y = grammar.vars.indexOf(r.rhs.get(0));
							int z = grammar.vars.indexOf(r.rhs.get(1));
							P[x][start-1][length-1] = Math.max( P[x][start-1][length-1],
												  			P[y][start-1][len1-1] * 
												  			P[z][start+len1-1][len2-1] * r.PROB);
						}
					}
				}
			}
		}
		return P;
	}
	
	/**
	 * Simple function to make algorithm more closely resemble pseudocode
	 * @param ls
	 * @return the length of the list
	 */
	public int length( List ls ) {
		return ls.size();
	}
	
	/**
	 * Print out the probability table produced by the CYK Algorithm
	 * @param probTable
	 * @param words
	 * @param g
	 */
	public void printProbTable( float[][][] probTable, List words, ProbUnrestrictedGrammar g ) {
		final int N = words.size();
		final int M = g.vars.size(); // num non-terminals in grammar
		
		for( int i=0; i < M; i++ ) {
			System.out.println("Table For : " + g.vars.get(i) + "(" + i + ")");
			for( int j=0; j < N; j++ ) {
				System.out.print(j + "| ");
				for( int k=0; k < N; k++ ) {
					System.out.print(probTable[i][j][k] + " | ");
				}
				System.out.println();
			}
			System.out.println();
		}
	}
	
	/**
	 * The probability table get's us halfway there, but this method can provide the 
	 * derivation chain that most probably derives the words provided to the parser.
	 * @param probTable
	 * @param g
	 * @return
	 */
	public ArrayList getMostProbableDerivation( float[][][] probTable, ProbUnrestrictedGrammar g ) {
		// TODO
		return null;
	}

} // end of CYKParse()