1. A method for aligning words in parallel segments, the method comprising: calculating a first probability distribution, utilizing a processor and a memory, according to a model estimate of word alignments within a first corpus comprising word-level unaligned parallel segments, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the first probability distribution; discriminatively re-ranking one or more sub-models associated with the modified model estimate according to word-level annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked sub-models associated with the modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; and wherein the comparing of the manual alignments to the hypothesized alignments comprises: comparing an updated weighting factor for each sub-model derived using the first corpus to randomly generated weighting factors; and selecting one of the updated weighting factor and the randomly generated weighting factor that generates a least amount of error.