the network parameters. Then the mapped features of two child nodes are fed into the ***Combiner*** sub-network to generate the feature representation of the parent node. $$\label{eq_com}
\mathbf{x}_{kl} = F_{com}([\mathbf{x}_k, \mathbf{x}_l]; \mathbf{W}_{com})$$ where $F_{com}$ is the network transformation and $\mathbf{W}_{com}$ denotes the corresponding parameters. Note that the parent node feature has the same dimensionality as the child node feature, allowing the procedure can be applied recursively.

***Interpreter*** is the neural network that interprets the relation of two nodes in the parsing tree. We note that the use of pooling operation in Eqn.  will cause the losing of spatial information which is helpful to structure and relation prediction. As a remedy, we design the context features to involve spatial context. Intuitively, the interpreter network attempts to integrate the feature of two nodes and their contextual information to represent the interaction relation of two entities, $$\label{eq_int}
\mathbf{g}_{kl} = F_{int}([\mathbf{x}_{kl}, \mathbf{b}_{kl}]; \mathbf{W}_{int})$$ where $F_{int}$ and $\mathbf{W}_{int}$ indicate the network and layer weights respectively. $\mathbf{b}_{kl}$ denotes the contextual information as follows, $$\label{eq_contextual}
\mathbf{b} = [b^{ang},b^{dis},b^{scal}]$$ where $b^{ang}$ and $b^{dis}$ reflect the spatial relation between two semantic entities, while $b^{scal}$ is employed to imply area relation of semantic entities. As illustrated in Fig. \[fig:context\], $b^{ang}$ 