# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 3 Deep Learning Based MQA Approaches
### Recursive Neural-Network-Based Models  
A recursive neural network is a DNN category invented by recursively applying the same set of weights to structured inputs, generating structured or scalar predictions by crossing a given structure over variable-sized input structures in a topological order. Recursive neural networks perform the same as RNNs in handling variable length inputs. The primary difference is that RNN can model the hierarchical structures in the training dataset.  
A recursive neural network architecture consists of a shared weight matrix and a binary tree structure, which allows the recursive network to learn sequences of word variations (see Figure 13). This network uses a variant of back propagation called back propagation through structure (BPTS). However, a recurrent neural tensor network (RNTN) computes a supervised target at each tree's node. The tensor (a matrix of more than two dimensions) part means that it computes gradients in a slightly different way, taking more information at each node into account by using the tensor to exploit information from another dimension.  
[PERSON], et al. [64] proposed a dependency-tree recursive neural network (DT-RNN) model that can compute distributed representations for the individual sentences within quiz bowl questions. They extended their method to join predictions across sentences to create a question-answering neural network with trans-s  
Figure 12: Architecture of gated recurrent unit.  
TAS). They claimed that, once sentence-level representations are combined, they return paragraph-level representations which give a good prediction compared to individual sentences. Their model is described as follows.  
They start by relating each word \(w\) in their vocabulary with a vector representation \(x_{w}\in\mathbb{R}^{d}\). Then, they store vectors as the columns of a \(d\times V\) dimensional word embedding matrix \(W_{e}\), where \(V\) is the size of the vocabulary. Their model considers dependency parse trees of question sentences.  
Each node \(n\) in the parse tree for a particular sentence is associated with a word \(w\), a word vector \(x_{w}\) and a hidden vector \(h_{n}\in\mathbb{R}^{d}\) of the same dimension as the word vectors. For internal nodes, this vector is a phrase-level representation, while at leaf nodes it is the word vector \(x_{w}\) mapped into the hidden space.  
Unlike in constituency trees, where all words reside at the leaf level, internal nodes of dependency trees are associated with words. Hence, the DT-RNN combines the current node's word vector with its children's hidden vectors to form \(h_{n}\). This procedure continues in a recursive way up to the root, which presents the whole sentence.  
They relate a separate \(d\times d\) matrix \(W_{t}\) with each dependency relation \(r\) in their dataset, and these matrices are learnt during training. They include an extra \(d\times d\) matrix, \(W_{v}\) to integrate the word vector \(x_{w}\) at a node into the node vector \(h_{n}\). For example, the hidden representation \(h_{helots}\) is  
\[h_{helots}=f(W_{v}\cdot x_{helots}+b), \tag{18}\]  
where \(f\) stands for a non-linear activation function such as \(tanh\) and \(b\) is a bias term.  
When all leaves are completed, they proceed to interior nodes with already processed children. Continuing from \"helots\" to its parent, \"called\", they compute  
\[h_{called}=f\big{(}W_{DOBJ}\cdot h_{helots}+W_{v}\cdot x_{called}+b\big{)} \tag{19}\]  
They repeat this process up to the root, which is  
\[h_{depended}=f\big{(}W_{NSUBB}\cdot h_{conomy}+W_{PREP}\cdot h_{on}+W_{v} \cdot x_{depended}+b\big{)} \tag{20}\]  
The composition equation for any node \(n\) with children \(K(n)\) and word vector \(x_{w}\) is  
\[h_{n}=f(W_{v}\cdot x_{w}+b+\sum_{k\in K(n)}W_{R(n,k)}\cdot h_{k}) \tag{21}\]  
where \(R(n,k)\) is the dependency relation between node n and child node \(k\).