103
We focus on FAQ-like questions and answers , and build our system around a noisy-channel architecture which exploits both a language model for answers and a transformation model for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .
 used for: system, FAQ-like questions and answers; used for: noisy-channel architecture, system; used for: noisy-channel architecture, language model; used for: noisy-channel architecture, transformation model
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:
 used for: system, FAQ-like questions and answers; used for: noisy-channel architecture, system; used for: noisy-channel architecture, language model; used for: noisy-channel architecture, transformation model