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We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features -LRB- e.g. a language model score -RRB- as well as binary features based on the block identities themselves , e.g. block bigram features .
 used for: maximum likelihood criterion, log-linear block bigram model; used for: real-valued features, log-linear block bigram model; conjunction: real-valued features, binary features; hyponym of: language model score, real-valued features; used for: binary features, log-linear block bigram model
Given options, please tell me the relationships of all the listed entity pairs.Output format is "relation1: word1, word2; relation2: word3, word4". 
Option: conjunction, feature of, hyponym of, used for, part of, compare, evaluate for 
Text: {0} 
 Entity Pairs:  maximum likelihood criterion, log-linear block bigram model; real-valued features, log-linear block bigram model; real-valued features, binary features; language model score, real-valued features; binary features, log-linear block bigram model 
Answer:
 used for: maximum likelihood criterion, log-linear block bigram model; used for: real-valued features, log-linear block bigram model; conjunction: real-valued features, binary features; hyponym of: language model score, real-valued features; used for: binary features, log-linear block bigram model