ii Dedicated to
نویسندگان
چکیده
This paper introduces dual decomposition as aframework for deriving inference algorithmsfor NLP problems. The approach relies onstandard dynamic-programming algorithms asoracle solvers for sub-problems, together witha simple method for forcing agreement be-tween the different oracles. The approachprovably solves a linear programming (LP) re-laxation of the global inference problem. Itleads to algorithms that are simple, in that theyuse existing decoding algorithms; efficient, inthat they avoid exact algorithms for the fullmodel; and often exact, in that empiricallythey often recover the correct solution in spiteof using an LP relaxation. We give experimen-tal results on two problems: 1) the combina-tion of two lexicalized parsing models; and2) the combination of a lexicalized parsingmodel and a trigram part-of-speech tagger.
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تاریخ انتشار 2010