denotes the cosine value of angle $\theta$ between the center of two semantic entities. $b^{dis}$ indicates the distance $\gamma$ of two centers (i.e. $\alpha_1$ and $\alpha_2$). $b^{scal}$ is the area rate of such two entities, where $b^{scal} = \beta_1 / \beta_2$. In practice, we normalize all of contextual information into a range of $[-1,1]$.

***Categorizer*** sub-network determines the relation of two merged nodes. Categorizer is a softmax classifier that takes relation feature $\mathbf{g}_{kl}$ as input, and predicts the relation label $y_{kl}$, $$\label{eq_rel_category}
y_{kl} = softmax(F_{cat}(\mathbf{g}_{kl}; \mathbf{W}_{cat}))$$ where $y_{kl}$ is the predicted relation probability vector, $F_{cat}$ denotes the network transformation and $\mathbf{W}_{cat}$ denotes the network parameters.

***Scorer*** sub-network measures the confidence of a merging operation between two nodes. It takes the enhanced feature $\mathbf{g}_{kl}$ as input and outputs a single real value $h_{kl}$. $$\label{eq_node_score}
\begin{split}
h_{kl} &= F_{score}(\mathbf{g}_{kl}; \mathbf{W}_{score}) \\
q_{kl} &= \frac{1}{1+exp(-h_{kl})}
\end{split}$$ where $F_{score}$ denotes the network transformation and $\mathbf{W}_{score}$ denotes the network parameters. $q_{kl}$ indicates the merging score of node $\{kl\}$. Note such score is important to the configuration discovery and is used to optimize the recursive structure in the training phase, as described in Sec.\[sub:rnn\_loss\].

![Incorporating the contextual representation into RsNN forward process. The upper row shows 