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In general , our CRF model yields a lower error rate than the HMM and Max-ent models on the NIST sentence boundary detection task in speech , although it is interesting to note that the best results are achieved by three-way voting among the classifiers .
 compare: CRF model, HMM and Max-ent models; evaluate for: error rate, CRF model; evaluate for: error rate, HMM and Max-ent models; evaluate for: NIST sentence boundary detection task, CRF model; evaluate for: NIST sentence boundary detection task, HMM and Max-ent models; feature of: speech, NIST sentence boundary detection task; used for: classifiers, three-way voting
Given a phrase that describes the relationship between two words, extract the words and the lexical relationship between them. The output format should be "relation1: word1, word2; relation2: word3, word4". 
Option: conjunction, feature of, hyponym of, used for, part of, compare, evaluate for 
Text: {0} 
Answer:
 compare: CRF model, HMM and Max-ent models; evaluate for: error rate, CRF model; evaluate for: error rate, HMM and Max-ent models; evaluate for: NIST sentence boundary detection task, CRF model; evaluate for: NIST sentence boundary detection task, HMM and Max-ent models; feature of: speech, NIST sentence boundary detection task; used for: classifiers, three-way voting