Recursive Neural Networks (RvNNs) are deep neural networks used for natural language processing. We get a Recursive Neural Network when the same weights are applied recursively on a structured input to obtain a structured prediction. Business leaders and technology professionals need to understand what is Recursive Neural Network, what it can do, and how it works. 

What Is a Recursive Neural Network?  

Deep Learning is a subfield of machine learning and artificial intelligence (AI) that attempts to imitate how the human brain processes data and gains certain knowledge. Neural Networks form the backbone of Deep Learning. These are loosely modeled after the human brain and designed to accurately recognize underlying patterns in a data set. If you want to predict the unpredictable, Deep Learning is the solution. 

Recursive Neural Networks (RvNNs) are a class of deep neural networks that can learn detailed and structured information. With RvNN, you can get a structured prediction by recursively applying the same set of weights on structured inputs. The word recursive indicates that the neural network is applied to its output.

Due to their deep tree-like structure, Recursive Neural Networks can handle hierarchical data. The tree structure means combining child nodes and producing parent nodes. Each child-parent bond has a weight matrix, and similar children have the same weights. The number of children for every node in the tree is fixed to enable it to perform recursive operations and use the same weights. RvNNs are used when there's a need to parse an entire sentence. 

To calculate the parent node's representation, we add the products of the weight matrices (W_i) and the children's representations (C_i) and apply the transformation f:

  \[h = f \left( \sum_{i=1}^{i=c} W_i C_i \right) \], where c is the number of children.

Recurrent Neural Network vs. Recursive Neural Networks

  • Recurrent Neural Networks (RNNs) are another well-known class of neural networks used for processing sequential data. They are closely related to the Recursive Neural Network. 
  • Recurrent Neural Networks represent temporal sequences, which they find application in Natural language Processing (NLP) since language-related data like sentences and paragraphs are sequential in nature. Recurrent networks are usually chain structures. The weights are shared across the chain length, keeping the dimensionality constant. 
  • On the other hand, Recursive Neural Networks operate on hierarchical data models due to their tree structure. There are a fixed number of children for each node in the tree so that it can execute recursive operations and use the same weights for each step. Child representations are combined into parent representations. 
  • The efficiency of a recursive network is higher than a feed-forward network. 
  • Recurrent Networks are recurrent over time, meaning recursive networks are just a generalization of the recurrent network.     

Recursive Neural Network Implementation

A Recursive Neural Network is used for sentiment analysis in natural language sentences. It is one of the most important tasks of Natural language Processing (NLP), which identifies the writing tone and sentiments of the writer in a particular sentence. If a writer expresses any sentiment, basic labels about the writing tone are recognized. We want to identify the smaller components like nouns or verb phrases and order them in a syntactic hierarchy. For example, it identifies whether the sentence showcases a constructive form of writing or negative word choices.  

A variable called 'score' is calculated at each traversal of nodes, telling us which pair of phrases and words we must combine to form the perfect syntactic tree for a given sentence. 

Let us consider the representation of the phrase -- "a lot of fun" in the following sentence.

Programming is a lot of fun.

An RNN representation of this phrase would not be suitable because it considers only sequential relations. Each state varies with the preceding words' representation. So, a subsequence that doesn't occur at the beginning of the sentence can't be represented. With RNN, when processing the word 'fun,' the hidden state will represent the whole sentence. 

However, with a Recursive Neural Network (RvNN), the hierarchical architecture can store the representation of the exact phrase. It lies in the hidden state of the node R_{a\ lot\ of\ fun}. Thus, Syntactic parsing is completely implemented with the help of Recursive Neural Networks. 

Benefits of RvNNs for Natural Language Processing

  • The two significant advantages of Recursive Neural Networks for Natural Language Processing are their structure and reduction in network depth. 
  • As already explained, the tree structure of Recursive Neural Networks can manage hierarchical data like in parsing problems. 
  • Another benefit of RvNN is that the trees can have a logarithmic height. When there are O(n) input words, a Recursive Neural Network can represent a binary tree with height O(log\ n). This lessens the distance between the first and last input elements. Hence, the long-term dependency turns shorter and easier to grab. 

Disadvantages of RvNNs for Natural Language Processing

  • The main disadvantage of recursive neural networks can be the tree structure. Using the tree structure indicates introducing a unique inductive bias to our model. The bias corresponds to the assumption that the data follow a tree hierarchy structure. But that is not the truth. Thus, the network may not be able to learn the existing patterns.
  • Another disadvantage of the Recursive Neural Network is that sentence parsing can be slow and ambiguous. Interestingly, there can be many parse trees for a single sentence. 
  • Also, it is more time-consuming and labor-intensive to label the training data for recursive neural networks than to construct recurrent neural networks. Manually parsing a sentence into short components is more time-consuming and tedious than assigning a label to a sentence. 

Our Learners Also Ask:

1. Is CNN recursive?

New models of CNN (Convolutional Neural Networks) feature fully recursive convolutional layers for a fast and accurate image. This approach is quick and does not require extra input channels like surface normal.  

2. What is the difference between CNN and RNN?

Both CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) form the basis for several AI applications that drive business value. CNN is commonly applied to analyzing visual images. RNN, on the other hand, is a network where connected nodes form a directed graph along a temporal sequence.

CNN has a different architecture from RNN. It uses fee-forward neural networks that use filters and pooling layers, while RNNs have built-in memory similar to a loop and an RNN feed returns to the network. CNNs are generally used to solve spatial data and image recognition problems, while RNNs are more apt to analyze temporal, sequential data like language, texts, or videos. 

3. Is RNN more powerful than CNN?

CNN is more powerful and faster and includes fewer feature compatibility than RNN. While CNN takes fixed-size inputs and gives fixed-size outputs, RNN can tackle arbitrary input/output lengths. 

4. How many types of neural networks are there?

There are three critical types of Neural Networks in Deep Learning:

  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
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