Paper: Coarse-grained Candidate Generation and Fine-grained Re-ranking for Chinese Abbreviation Prediction

ACL ID D14-1202
Title Coarse-grained Candidate Generation and Fine-grained Re-ranking for Chinese Abbreviation Prediction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Correctly predicting abbreviations given the full forms is important in many natu- ral language processing systems. In this paper we propose a two-stage method to find the corresponding abbreviation given its full form. We first use the contextual information given a large corpus to get ab- breviation candidates for each full form and get a coarse-grained ranking through graph random walk. This coarse-grained rank list fixes the search space inside the top-ranked candidates. Then we use a sim- ilarity sensitive re-ranking strategy which can utilize the features of the candidates to give a fine-grained re-ranking and se- lect the final result. Our method achieves good results and outperforms the state-of- the-art systems. One advantage of our method is that it only needs weak super- vision ...