  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.

Computation and Language;Artificial Intelligence;Information Retrieval