Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information

The present paper describes a robust approach for abbreviating terms. First, in order to incorporate non-local information into abbreviation generation tasks, we present both implicit and explicit solutions: the latent variable model, or alternatively, the label encoding approach with global information. Although the two approaches compete with one another, we demonstrate that these approaches are also complementary. By combining these two approaches, experiments revealed that the proposed abbreviation generator achieved the best results for both the Chinese and English languages. Moreover, we directly apply our generator to perform a very different task from tradition, the abbreviation recognition. Experiments revealed that the proposed model worked robustly, and outperformed five out of six state-of-the-art abbreviation recognizers.