It was a new beginning when computers started to understand the languages that we know! Though it was due to rising technology but the fact was quite fascinating. This all was brought about by the introduction of natural language processing which flipped the coin. With the development of artificial intelligence and natural language processing, the world saw various new inventions and innovative ideas.

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With the growth of technology, there are various developments. These developments have also increased the demand for more and more innovations. As technology increases, there is an increased urge in almost all the sectors, for progress. One such technology that has revolutionized the industry is – NLP (Natural Language Processing). 

NLP is the cross-field of machine learning, computer science, and artificial intelligence. It has emerged to make the computers learn about the processing of languages so that they can understand languages like humans. The main AIM of NLP was to make the work of humans easier by teaching computers. But now, its applications have increased at a higher pace.

There is wide use of natural language processing, like in sentiment analysis, voice recognition, automatic text summarization, virtual assistance, and much more. These applications help various sectors and have been a part of various other applications. These can be used in hospitals, call centers, police stations, for taking attendance using voice recognition, and in many other fields. 

This technology teaches the computer to learn and understand the human language. As artificial intelligence provides the machines with the opportunity to humans, similarly natural language processing helps the machines to communicate like humans. There are various examples of the same, e.g., Alexa, Siri, Cortana. These are in trend. This also helps in text-filtering and machine translation.

NLP is highly linked with machine learning and deep learning. Any progress in these two fields, automatically, leads to the benefit in natural language processing. It is broadly divided into three categories. These three are the basic building blocks of natural language processing as they have laid the foundation of NLP.

• Natural Language Generation – The computer generates the language or what we call the natural language, i.e., the common language used by humans. It is taught to do so by proper training.

• Speech Recognition – Now one of the common and essential thing required is the conversion of speech to text. This is required as the analysis on speech can’t be done in audio format, e.g., sentiment analysis. For this, it is required to convert the speech into text. Then using the text we got, we can perform various operations on it, to get the output in the desired form. 

• Understanding the Natural Language – The generation of natural language and the conversion of voice to text is done. Then there is the need for understanding what we say, by the computer. It is required so that the computer can process our queries and give us the desired output. Until and unless the computer won’t understand our language the other two factors are not significant.

One of the finest examples of this is Google Assistant. We all use that service provided by google in our day-to-day life. It understands our query when we speak to it, and the most fascinating thing is, it can understand and process many languages like English, Hindi, Tamil, Bengali, and much more.

It listens to our language and processes it to get what we are asking for. Then it translates our speech to text to search for the solution of our query. This is done within a few seconds. The work is quite efficient and effective. After all this, it generates the result in text format, and that too in the particular language which we require.

Like Google Assistant, there are many other widely used tools and services available which are based on the principle of natural language processing.

It seems quite awesome to train machines to understand and generate human languages. But this is not easy. The human language is very different and difficult to read and understand. It is said that the human language comprises lots of symbols. It is discrete and a simple message can be conveyed in more than one way. This makes the task of understanding natural language and speech recognition difficult.

It should be noted that the human language is very complex whereas the machines follow the binary pattern to work internally. This is very the brainstorming is required. The tedious and interesting task is to make the machine learn something new and then explore it by continuous usage.

All this comes with the proper training of machines. Natural Language Processing requires the machines to have the technical pinch of machine learning, deep learning, and artificial intelligence. In this way, the machines would get a proper set of a technological stack for further processing. 

The Two Primary Techniques

We know that whatever we say is text. But a language that a human speaks should be valid so that the machine could understand and process it. Then there comes the need to define what valid language is? This is done by the two techniques – Syntactic or Syntax analysis and Semantic analysis. These two techniques are required to understand the natural language and make it available for processing by the machines.

When we type or say any sentence, the two major things that we consider are – the sentence should be grammatically correct, i.e., it should be structured properly and the other thing is that the sentence should have some meaning. These two things are syntax and semantic respectively. While one deals with the proper organization of words, the other ensures that the sentence is not useless or meaningless.

The syntactic analysis also called syntax analysis, is used to check for the structure of the words. It is used to check whether the rules of grammar are followed or not. It is necessary to follow the rules of formal grammar so that the machine could understand the language properly. In this way, it is one of the important pre-task to be done. 

The rules of grammar can’t be applied to every single word, it is applied to a group of words. This is what we can refer to as the category of words. Then the sentence formation is checked. It is ensured that the language is valid syntactically. 

The grammar is checked, i.e., the proper orientation of the subject, object, and the verb is verified. Along with this, the proper use of the verb, tense, noun, adjective, etc. is also checked. This all is used to determine whether the sentence orientation is correct or not.

The semantic analysis deals with the meaning of the sentence. The meaning generation from a sentence is different for humans and machines. Humans can interpret the meaning of the sentence by using their knowledge and skills as they are familiar with the language. Whereas, machines are dependent on logic or what is referred to as meaning. 

Machines cannot interpret the meaning from the unstructured or invalid sentence as they work purely on logic and the dataset values on which they are trained. Hence, the semantic analysis deals with the processing and understanding of words, symbols, and various signs of which the sentence is formed.

This is one of the most difficult processes in NLP and yet the technicians are developing a more effective and easy approach for semantic analysis.

We can go with the example of speech recognition. While using google assistant, you might have observed that the machine listens to all your queries and can convert them to text. But sometimes, it may not be able to provide the output and will show the message – “No result found”. This happens because the machine wasn’t able to understand the meaning of your query.

This is what semantic analysis focuses on, to generate meaningful text. This is required so that the queries can be understood by the machine.

How do the machines understand the text?

Natural language processing comes with various steps that are followed to make the machine understand the text. The following is a list of all the steps followed. This is quite interesting to know how the text in our natural language is understood by the machines that generally work in binary format. 

Parsing

Parsing a sentence means breaking the sentence into its basic components. This means the analysis of the sentence into its basic parts by the computer. The sentence is divided into nouns, verbs, tenses, and so on. This results in the generation of a tree structure that is referred to as the Parse Tree. 

Here, S = Sentence,

N = Noun,

VP = Verb Phrase,

DT = Determiner,

V = Verb

The above-mentioned figure is the Parse Tree for the sentence – ‘Barbar is busy’. In this way, the parsing of a sentence is done. This breaks the sentence into verbs, nouns, determiner, etc. so that the machine can understand the orientation of the sentence. This is essential for semantic analysis as well.

This hierarchical ordering also provides the grammatical relationship of the sentence.

Stemming

It can be termed as refining the words. The words are brought closer to their word stem, i.e., the words are reduced. All the additional unnecessary words are removed. There are various algorithms for stemming like the Porter algorithm. This is a very crucial step. In this, the words to be removed should not change the meaning of the sentence.

Text Segmentation

This depends on the complexity of the language used. Generally, it means the transformation of text into meaningful sentences. The words are segmented properly so that they would reflect proper meaning. In this way, the proper organization and meaning of words are ensured.

The text is categorized into basic categories. There are some pre-defined categories in which the text is categorized. This is known as the Named Entity Recognition (NER). 

Then the text that is categorized is taken and the relationship is drawn between these words. This is called relationship extraction. As the words have no meaning on their own. To get the meaning, they need to be organized and relate to each other. For this, it is necessary to process them and find out the relationship between them.

Then comes one of the major parts, i.e., sentiment analysis. As we know that the words reflect different meanings through the emotions with which they are said. And the machine can’t understand the emotions of humans directly. This can be achieved by studying the text properly.

In this process, the text of the person is analyzed, i.e., various positive and negative words reflect the emotions of the person. They generate the flow of the speaker or the writer. This is widely used during surveys, interviews, etc.

Moreover, during online sessions, interviews, surveys, and much more, sentimental analysis is way too important. 

Deep Learning

To summarize the cluster of words, one of the requirements is the knowledge of deep learning. It provides a deep insight into the meaning of the text or speech that the person gives as input. 

With the help of deep learning, the analysis of speech, understanding natural language, and its generation becomes quite effective and meaningful. Hence, this provides a wide view of speech recognition, text analysis, and much more.

Overall, NLP has transformed the industry by making us interact with computers in a much efficient and effective manner. There is a wide scope of NLP in the technology industry and is widely used in all sectors. 

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