What is NLP? An Introduction to Natural Language Processing

Have you ever had this thought that when you start searching for something on your Google SERP, it starts predicting alternative searches for you, and most of the time, it precisely suggests things you were looking for? How it also suggests searches on the basis of your previous searches? Well, that is the art of Natural Language Processing. The ability of SERP to anticipate your search is brought by NLP. We have entered an era where there is no field left untouched by the wonders of machine learning and AI. The tech-based world we live in has an intuitive response to all our queries, and concerns even before we get to raise them. And as we are so used to its accommodation in our life, we have forgotten the intricacy behind such things coming into existence. We have come a long way from making communication easy from one human to another to making communication even easier from human to machine.

Gone, are the days when we were only able to command machines to incorporate tasks. With technological advancements skyrocketing today, we can literally have machines and AI learning modules talk to us and anticipate our conversations like never before.

If you are intrigued to learn about this aspect or to know learn about its process, you have come to the right place. Let's dive into the blog, without wasting any further time.

What is NLP?

Natural Language Processing (NLP) refers to the machine’s ability to read and perceive text in the same way a human brain does. NLP is a subfield of language processing, AI, and machine learning that primarily focuses on deciphering user instructions. In other words, it manages language-based communication between computers and people. It entails the creation of algorithms and models that give human language value and meaning to the computer, enabling it to comprehend it. It assists the computer in analyzing and processing a sizable amount of data supplied in natural language. A wide range of text or voice may be transformed into useful information for the computer using NLP.

What is the importance of NLP?

NLP gained a lot of importance because of its feature of machine translation. It is highly used to translate human language into machine code. It has helped immensely to deal with language constraints. As time is passing by, the number of datasets is increasing rapidly. We live in a world built on data and hence in order to manipulate, comprehend data and apply such data reference, NLP comes into action. Its proficiency in the automatic summarization of existing data makes it useful in converting data from one language to another.

NLP also works precisely to understand which sentiment of the text has been delivered. NLP methods are extremely valuable for conducting sentiment analysis of the text. Due to its potency in sentiment analysis, its other practical usage is also seen in spam filtering, text-to-speech converters, question answering, voice recognition, and information retrieval.

How does it work?

We are making use of NLP modules knowing or unknowingly through Google, Siri, and Alexa, moreover that we have also been operating chatbots these days which are highly influenced by NLP methods. These NLP methods rely on deep learning modules and different algorithms to understand and accordingly provide the anticipated end results. Natural Langauge Processing interprets the human language into structural data.

This interpretation of data is followed by a higher level of NLP capacities which are as follows:

1. Tokenization

Tokenization is the process of splitting text data into smaller parts. Tokenization helps in breaking paragraphs into sentences, sentences to words, and further words to punctuations. This helps the NLP learning modules to interpret human language even more easily.

2. Text Cleaning and Preprocessing:

The technique of text cleaning is referred to the process of removing or cutting out irrelevant punctuations from the sentence. It removes unnecessary noise from the text for easy understanding of the data for the NLP models that are learning the text. The process begins with converting all the text to lower caps and removing punctuation.

3. Lexical Analysis:

Lexical Analysis is the method of analyzing each token to understand its lexical attributes. Lexical attributes such as POS, lemma, and morphological features. Part of Speech is extremely important for understanding the grammatical features of the data text.

4. Syntactic Analysis:

Syntactic analysis or syntax analysis is known as the third phase of the NLP methods. The intention behind this analysis is to understand the exact meaning or the very dictionaries meaning of the text data.

The analysis is done to extract meaning from the text. It helps in the hierarchical representation of the text presented in the form of a parse tree.

5. Semantic Analysis:

Semantic Analysis is a sub-field of Natural Language Processing. It focuses greatly on the meaning of the text to form the grammatical structure of the sentence and not just of the word. This process is done by semantic analysis by identifying the relationship between words from the text.

6. Language and Context Modelling:

Language modeling represents to be the backbone of Natural Language Processing. LM and context modeling are done to understand the intention behind the given text. These models are essential for understanding tasks like speech recognition, speech-to-text converter, machine translation, and text generation.

7. Machine Learning and Deep Learning:

Natural Language Processing methods involves Deep learning and machine learning models using CNN (convolutional neural networks). Deep learning in NLP is used to understand and execute tasks like sentiment analysis, text classification even during unsupervised learning. It is also greatly used for text prediction and giving human like feel to the result generated.

8. Sentiment Analysis:

Sentiment Analysis is done to perceive the tone or the manner in which the text is written in. Sentiment Analysis helps with the understanding of as to why the text was written, to whom was it written, what was the motive behind the text and how it is to be perceived.

9. Stop word removal:

Stop word removal is the process of removing words which are viewed as unnecessary or insignificant by the system. Words such as a, as, an, the, are often eliminated to reduce noise in the text and to understand the text to its fullest, as it is repeated frequently and add no unique information to the text.

10. Named Entity Recognition:

NER helps with name, people, places, organization, dates and more which have a repetitive nature of occurrence in the given text.

Conclusion

NLP is vast and has various sub-fields and specific applications under it. Advancements in NLP has greatly contributed to a new phase of technology and improved performance. Here I will, concluding our blog, if you are in search of a precised way to learn and understand more about NLP and its magic, Itvedants’s Data Science and Analytics with AI is the course you are looking for.

Thank you so much for your patient reading and sticking to the end. I will be waiting for the same enthusiasm at the beginning of our next blog.