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A question about the maximal matching

Hi~ Thanks for sharing the source code.

I noticed that you obtain the maximal matching by simply taking the tag with the maximal cooccurrence of each token.

mmi-tagger/evaluate.py

Lines 61 to 66 in 40e2299

    
           def get_majority_mapping(tseqs, zseqs): 
        
               cooccur = count_cooccurence(tseqs, zseqs) 
        
               mapping = {} 
        
               for z in cooccur: 
        
                   mapping[z] = max(cooccur[z].items(), key=lambda x: x[1])[0] 
        
               return mapping

But, since the motivation of this paper is to maximize mutual information, would it perform better if you search for the matching which maximizes the mutual information? i.e., $\arg\max_{(T,Z)}{\operatorname{MI}{(T,Z)}}$.

Maybe minimum cost max flow algorithm can be applied to search for it, I'm not sure.

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