Neural Networks Models for Entity Discovery and Linking
iFLYTEK Research（科大讯飞研究院）；University of Science and Technology of China（中国科学技术大学）；York University（约克大学）
英文摘要：
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
