7. The method of claim 6 , wherein the updating of the predefined weight comprises determining a weight for each node of the one or more first nodes and the one or more second nodes using 
 minΣ( i,j )∈ E|a i −a j | where E corresponds to an extended keyword graph, a i corresponds to a weight by which a unigram probability estimate for keyword I is incremented, and a j is defined for keywords in set M=L∪D, L and D correspond to nodes and edges.
7. The method of claim 6 , wherein the updating of the predefined weight comprises determining a weight for each node of the one or more first nodes and the one or more second nodes using 
 minΣ( i,j )∈ E|a i −a j | where E corresponds to an extended keyword graph, a i corresponds to a weight by which a unigram probability estimate for keyword I is incremented, and a j is defined for keywords in set M=L∪D, L and D correspond to nodes and edges. 8. The method of claim 7 , wherein the determining of the weight is constrained by one or more of the following constraints a i ≥ a j , ∀ i = 1 , … ⁢ ,  L  , ∀ j = 1 , … ⁢ ,  D  a k ≥ 0 , ∀ k = 1 , … ⁢ , M ∑ k = 1  M  ⁢ a k ≤ l ∑ i ∈ M ⁢ ( p i + a i ) + ∑ j ∈ J ⁢ p j - ∑ k = 1  M  ⁢ a k  J  = 1 where p j corresponds to a unigram probability for keyword j∈V, wherein “V” denotes a language model vocabulary and |V| denotes vocabulary size, L corresponds to the first set of keywords in “V” that are recognized from visual content in the multimedia; D corresponds to the second set of keywords in “V” that are obtained after expanding keywords in “L”; |L|, |M|, |J|, and |D| correspond to a size of the first set of keywords and the size of the second set of keywords.