A Late Fusion Approach to Cross-lingual Document Re-ranking

摘要

The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to standard bag-of-words based models. Cross-lingual information retrieval is an example of where such a model is required, as content or concepts often need to be matched across languages. To overcome this problem, a conceptual model has been adopted in ranking an entire corpus which normally exploits latent/implicit features of the text. One of the drawbacks of this model is that the computational cost is significant and often intractable in modern test collections. Therefore, approaches utilizing concept-based models for re-ranking initial retrieval results have attracted a considerable amount of study, in particular the latent concept model. However, fitting such a model to a smaller collection is less meaningful than fitting it into the whole corpus. This paper proposes a late fusion method which incorporates scores generated by using external knowledge to enhance the space produced by the latent concept method. This method is further demonstrated to be suitable for multilingual re-ranking purposes. To illustrate the effectiveness of the proposed method, experiments were conducted over test collections across three languages. The results demonstrate that the method can comfortably achieve improvements in retrieval performance over several re-ranking methods.