NLP tools for Artificial Intelligence

There is consistent research going on to improve Artificial Intelligence (AI) so that it can understand the human speech naturally. In computer science, it is called as Natural Language Processing (NLP). In recent years, NLP has gained momentum because of the use of neural networks. With the help of these networks, there has been increased precision in predictions of tasks such as analyzing emotions.

With its advent in the world of computer science, a non-linear model for artificial computation has been created that replicates the neural framework of the brain. In addition, this structure is capable of performing NLP tasks such as visualization, decision-making, prediction, classification, etc.

Artificial Neural Networks that benefit NLP

An artificial neural network combines the use of its adjoined layers, which are input, output and hidden (it may have many layers), to send and receive data from input to the output layer through the hidden layer. While there are many types of artificial neural networks (ANN), the 5 prominent ones are explained in brief below:

1. Multilayer perceptron (MLP)

An MLP has more than one hidden layers. It implements the use of a non-linear model for activating the logistic or hyperbolic tangent function to classify data, which is linearly inseparable otherwise. All nodes in the layer are connected to the nodes following them so that the network is completely linked. Machine translation and speech recognition NLP applications fall under this type of ANN.

2. Convolutional Neural Network (CNN)

A CNN neural network offers one or many convolutional (looped or coiled) hidden layers. It combines several MLPs to transmit information from input to the output. Moreover, convolutional neural networks can offer exceptional results without the need for semantic or syntactic structures such as words or sentences based on human language. Moreover, it has a wider scope of image-based operations.

3. Recursive neural network (RNN)

A recursive neural network is a repetitive way of application of weight inputs (synapses) over a framework to create an output based on scalar predictions or predictions based on varying input structures. It uses this transmission operation by crossing over a particular framework in topological order. Simply speaking, the nodes in this layer are connected using a weight matrix (traversing across the complete network) and a non-linear function such as the hyperbolic function ‘tanh.’

4. Recurrent Neural Network (RNN):

Recurrent neural networks provide an output based on a directed cycle. It means that the output is based on the current synapses as well as the previous neuron’s synapses. This means that the recorded output from the previous information will also affect the current information. This arbitrary concept makes it ideal for speech and text analysis.

5. Long short-term memory (LSTM):

It is a form of RNN that models a long-range form of temporal layers accurately. It neglects the use of activation functions so it does not modify stored data values. This neural network is utilized with multiple units in the form of “blocks,” which regulate information based on logistic function.

With an increase in AI technology, the use of artificial neural networks with NLPs will open up new possibilities for computer science. Thus, it will eventually give birth to a new age where computers will be able to understand humans better.

Artificial Neural Networks – The basics

Artificial Neural Networks are simulations that are derived from the biological functions of ‘neurons’ which are present in the brain. Thus, Artificial Neural Networks are essentially artificial neurons configured to carry out a specific task. ANN has gained a lot of popularity as it is used to model non-linear processes.

Artificial Neural Networking allows solving problems like clustering, classification, pattern recognition, prediction, and determining outliers. This has made ANN a very useful tool.

How does ANN work?

Artificial Neural Networks acquires knowledge through learning continuously. Like in humans, the knowledge acquired is stored in the artificial neurons designed within the ANN and used to perform the required task. ANN has a wide range of syntax, semantics, and speech-tasks which help ANN solve a wide range of problems.

Some of the interesting applications of ANN are discussed below.

1. Text Classification

Applications like web searches, language identification are some of the applications that use text classification. Neural Networks are widely employed for this type of classification. Experts agree that deep learning can be applied to enhance the value delivered by text classification. Artificial Neural Networks can be applied from character-level inputs as well as abstract text content.

CovNets or Convolutional Networks can deliver good outcomes in text classification without prior knowledge of words or phrases by applying them along with deep learning and Neural Networks.

2. Semantic Parsing

Artificial Neural Networks can be actively helpful in answering questions. A Q&A system will automatically answer any question asked in natural languages like definition questions, biographical questions and so on. Using Neural Networks in these systems makes it possible to maintain a high performing question answering system.

Developers have released semantic parsing framework for answering questions using a specific knowledge base. ANN uses this framework to quickly identify the type of questions and then answers it using semantic matching. There are other frameworks available which can further improve neural networks’ performance in this field.

3. Speech Recognition

Voice technology has advanced and now it is used for automated telephone conversations, speech-only computing, and much more. Neural Networks are being used extensively in this area. neural networks can specifically be programmed to handle multiple types of queries over a wide range and with continuous learning, neural networks help you achieve a great speech recognition software.

4. Character Recognition

Character recognition has become vital in today’s world across different industry verticals. There are many practical applications in this realm. Some instances include character recognition on receipts, invoices, checks, or legal billing. The Character Recognition framework for Artificial Neural Networks has been effectively used in this field and tests have shown the accuracy to be above 85%.

5. Spell Check

Text editors help you find out misspelled words to help you rectify them. Neural networks have been incorporated in many of these text editors nowadays to provide easier spell checks. It uses the personalized spell check framework and it outperforms many other text editors that don’t use Artificial Neural Networks today.

To conclude, we can say that Artificial Neural Networks are very versatile and make a lot of jobs easier in different functions within an enterprise.