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We compare two language modelling toolkits , the CMU and the SRI toolkit and arrive at three results : 1 -RRB- word-lemma based feature function models produce better results than token-based models , 2 -RRB- adding a PoS-tag feature function to the word-lemma model improves the output and 3 -RRB- weights for lexical translations are suitable if the training material is similar to the texts to be translated .
 hyponym of: CMU and the SRI toolkit, language modelling toolkits; compare: word-lemma based feature function models, token-based models; part of: PoS-tag feature function, word-lemma 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:  CMU and the SRI toolkit, language modelling toolkits; word-lemma based feature function models, token-based models; PoS-tag feature function, word-lemma model 
Answer:
 hyponym of: CMU and the SRI toolkit, language modelling toolkits; compare: word-lemma based feature function models, token-based models; part of: PoS-tag feature function, word-lemma model