In this paper a robust, adaptive approach for mining parallel sentences from a bilingual comparable news collection is described. Sentence length models and lexicon-based models are combined under a maximum likelihood criterion. Specific models are proposed to handle insertions and deletions that are frequent in bilingual data collected from the web. The proposed approach is adaptive, updating fhe iranslation lexicon iteratively using the mined parallel data to get better vocabulary coverage and translation probability parameter estimation. Experiments are carried out on 10 years of Xinhua bilingual news collection. Using the mined data, we get significant improvement in word-to-word alignment accuracy in mnchine translation modeling.