The USTC_NELSLIP Systems for Trilingual Entity Detection and Linking Tasks at TAC KBP 2016
Table 2: All input feature vectors used in the NN ranking model.
['[EMPTY]', 'dim', '[BOLD] feature']
For each detected mention m, the candidate generation module generates a list of K candidates as {c1,⋯,cK}. Furthermore, we use a softmax function to compute a posterior distribution of all candidates in the list as follows: In this work, we use the EDL2015 training data set, which contains less than 200 labeled documents to train the neural net ranking model for each language. We choose to use 2 hidden layers: the first layer consists of 512 units while the second layer is composed of 256 units. Each hidden unit use the sigmoid nonlinear activation function. We adopt a mini-batch AdaDelta with the mini-batch size of 8. Similar to mention detection, we have also trained 5 different models from different subsets and different random initialization. We have found that an ensemble of five NN ranking yields a small performance gain in the entity linking tasks.