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.

本论文描述了 USTC_NELSLIP 系统，在 2016 年 TAC 知识库填充 (KBP) 竞赛中提交的 Trilingual Entity Detection and Linking (EDL) 赛道，使用了两种方法进行实体发现和提及检测 (即使用 条件 RNNLM 和基于注意力的编码器解码器框架)，将实体链接 (EL) 系统分为两个模块：基于规则的候选生成和神经网络概率排名模型。此外，还使用一些简单的字符串匹配规则进行 NIL 聚类，在结束时，我们的最佳系统在整个类型提及 CEAF 加度量中取得了 0.624 的 F1 值。