Bilingual corpora are an essential resource used to cross the language barrier in multilingual Natural Language Processing (NLP) tasks. Most of the current work makes use of parallel corpora that are mainly available for major languages and constrained areas. Comparable corpora, text collections comprised of documents covering overlapping information, are however less expensive to obtain in high volume. Previous work has shown that using comparable corpora is beneficent for several NLP tasks. Apart from those studies, we will try in this thesis to improve the quality of comparable corpora so as to improve the performance of applications exploiting them. The idea is advantageous since it can work with any existing method making use of comparable corpora. We first discuss in the thesis the notion of comparability inspired from the usage experience of bilingual corpora. The notion motivates several implementations of the comparability measure under the probabilistic framework, as well as a methodology to evaluate the ability of comparability measures to capture gold-standard compara- bility levels. The comparability measures are also examined in terms of robustness to dictionary changes. The experiments show that a symmetric measure relying on vo- cabulary overlapping can correlate very well with gold-standard comparability levels and is robust to dictionary changes. Based on the comparability measure, two methods, namely the greedy approach and the clustering approach, are then developed to improve the quality of any given comparable corpus. The general idea of these two methods is to choose the high- quality subpart from the original corpus and to enrich the low-quality subpart with external resources. The experiments show that one can improve the quality, in terms ix of comparability scores, of the given comparable corpus by these two methods, with the clustering approach being more efficient than the greedy approach. The enhanced comparable corpus further results in better bilingual lexicons extracted with the stan- dard extraction algorithm. Lastly, we investigate the task of Cross-Language Information Retrieval (CLIR) and the application of comparable corpora in CLIR. We develop novel CLIR models extending the recently proposed information-based models in monolingual IR. The information-based CLIR model is shown to give the best performance overall. Bilin- gual lexicons extracted from comparable corpora are then combined with the existing bilingual dictionary and used in CLIR experiments, which results in significant im- provement of the CLIR system.
