Ordering Prenominal Modifiers with a Reranking Approach
Jenny Liu, Aria Haghighi · 2011
In this work, we present a novel approach to the generation task of ordering prenominal modifiers. We take a maximum entropy reranking approach to the problem which admits arbitrary features on a permutation of modifiers, exploiting hundreds of thousands of features in total. We compare our error rates to the state-of-the-art and to a strong Google n-gram count baseline. We attain a maximum error reduction of 69.8 % and average error reduction across all test sets of 59.1 % compared to the state-of-the-art and a maximum error reduction of 68.4 % and average error reduction across all test sets of 41.8 % compared to our Google n-gram count baseline. 1