| Title | Learning entity representation for entity disambiguation |
| Authors | He, Zhengyan Liu, Shujie Li, Mu Zhou, Ming Zhang, Longkai Wang, Houfeng |
| Affiliation | Key Laboratory of Computational Linguistics, Peking University, Ministry of Education, China Microsoft Research Asia, China |
| Issue Date | 2013 |
| Citation | 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013.Sofia, Bulgaria,2(30-34). |
| Abstract | We propose a novel entity disambiguation model, based on Deep Neural Network (DNN). Instead of utilizing simple similarity measures and their disjoint combinations, our method directly optimizes document and entity representations for a given similarity measure. Stacked Denoising Auto-encoders are first employed to learn an initial document representation in an unsupervised pre-training stage. A supervised fine-tuning stage follows to optimize the representation towards the similarity measure. Experiment results show that our method achieves state-of-The-art performance on two public datasets without any manually designed features, even beating complex collective approaches. ? 2013 Association for Computational Linguistics. |
| URI | http://hdl.handle.net/20.500.11897/412017 |
| Indexed | EI |
| Appears in Collections: | 计算语言学教育部重点实验室 |
