  Learning distributed representations for relation instances is a central
technique in downstream NLP applications. In order to address semantic modeling
of relational patterns, this paper constructs a new dataset that provides
multiple similarity ratings for every pair of relational patterns on the
existing dataset. In addition, we conduct a comparative study of different
encoders including additive composition, RNN, LSTM, and GRU for composing
distributed representations of relational patterns. We also present Gated
Additive Composition, which is an enhancement of additive composition with the
gating mechanism. Experiments show that the new dataset does not only enable
detailed analyses of the different encoders, but also provides a gauge to
predict successes of distributed representations of relational patterns in the
relation classification task.
