TitleCoarse-grained candidate generation and fine-grained re-ranking for chinese abbreviation prediction
AuthorsZhang, Longkai
Wang, Houfeng
Sun, Xu
AffiliationKey Laboratory of Computational Linguistics, Peking University, Ministry of Education, China
Issue Date2014
Citation2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014.Doha, Qatar.
AbstractCorrectly predicting abbreviations given the full forms is important in many natural 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 abbreviation 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 similarity sensitive re-ranking strategy which can utilize the features of the candidates to give a fine-grained re-ranking and select the final result. Our method achieves good results and outperforms the state-ofthe- Art systems. One advantage of our method is that it only needs weak supervision and can get competitive results with fewer training data. The candidate generation and coarse-grained ranking is totally unsupervised. The re-ranking phase can use a very small amount of training data to get a reasonably good result. ? 2014 Association for Computational Linguistics.
Appears in Collections:计算语言学教育部重点实验室

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