ReFr: A New Open-Source Framework for Building Reranking Models by Dan Bikel and Keith Hall.
From the post:
We are pleased to announce the release of an open source, general-purpose framework designed for reranking problems, ReFr (Reranker Framework), now available at: http://code.google.com/p/refr/.
Many types of systems capable of processing speech and human language text produce multiple hypothesized outputs for a given input, each with a score. In the case of machine translation systems, these hypotheses correspond to possible translations from some sentence in a source language to a target language. In the case of speech recognition, the hypotheses are possible word sequences of what was said derived from the input audio. The goal of such systems is usually to produce a single output for a given input, and so they almost always just pick the highest-scoring hypothesis.
A reranker is a system that uses a trained model to rerank these scored hypotheses, possibly inducing a different ranked order. The goal is that by employing a second model after the fact, one can make use of additional information not available to the original model, and produce better overall results. This approach has been shown to be useful for a wide variety of speech and natural language processing problems, and was the subject of one of the groups at the 2011 summer workshop at Johns Hopkins’ Center for Language and Speech Processing. At that workshop, led by Professor Brian Roark of Oregon Health & Science University, we began building a general-purpose framework for training and using reranking models. The result of all this work is ReFr.
An interesting software package and you are going to pick up some coding experience as well.