Cross-Language Microblog Retrieval using Latent Semantic Modeling

Microblogging has become one of the major tools of sharing real-time information for people around the world. Finding relevant information across different languages on microblogs is highly desirable especially for the large number of multilingual users. However, the characteristics of microblog content pose great challenges to the existing cross-language information retrieval approaches. In this paper, we address the task of retrieving relevant tweets given another tweet in a different language. We build parallel corpora for tweets in different languages by bridging them via shared hashtags. We propose a latent semantic approach to model the parallel corpora by mapping the parallel tweets to a low-dimensional shared semantic space. The relevance between tweets in different languages is measured in this shared latent space and the model is trained on a pairwise loss function. The preliminary experiments on a Twitter dataset demonstrate the effectiveness of the proposed approach.