Generalized Abbreviation Prediction with Negative Full Forms and Its Application on Improving Chinese Web Search

In Chinese abbreviation prediction, prior studies are limited on positive full forms. This lab assumption is problematic in realworld applications, which have a large portion of negative full forms (NFFs). We propose solutions to solve this problem of generalized abbreviation prediction. Experiments show that the proposed unified method outperforms baselines, with the full-match accuracy of 79.4%. Moreover, we apply generalized abbreviation prediction for improving web search quality. Experimental results on web search demonstrate that our method can significantly improve the search results, with the search F-score increasing from 35.9% to 64.9%. To our knowledge, this is the first study on generalized abbreviation prediction and its application on web search.