Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Table 2: Details of dimensions of tensors constructed for each dataset used in the experiments.
['[BOLD] Dataset', 'X1 [ITALIC] shape', 'X2 [ITALIC] shape', 'X3 [ITALIC] shape']
Datasets: We run our experiments on three datasets. The first dataset (Shootings) is a collection of 1,335 documents constructed from a publicly available database of mass shootings in the United States. The second is New York Times Sports (NYT Sports) dataset which is a collection of 20,940 sports documents from the period 2005 and 2007. And the third dataset (MUC) is a set of 1300 Latin American newswire documents about terrorism events. However, in order to properly analyze and evaluate the model, we consider only the 50 most frequent relations in the datasets and their corresponding OpenIE extractions. This is done to avoid noisy OpenIE extractions to yield better data quality and to aid subsequent manual evaluation of the data.