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What IS Natural Language Processing (NLP)? Approaches of NLP applications - Real-World Cases

What IS Natural Language Processing (NLP)? Best SAP and DevOps institute in Kerala!
What IS Natural Language Processing (NLP)?

Are you familiar with C3PO, machines talking to us in a human-like manner, smart assistance, and calls made over the Internet? All these have 1 thing in common none of them are human. 

How do they sound and seem like humans?

How do they respond too intelligently?

How are they so articulate?

Here is your answer…

What IS Natural Language Processing (NLP)?

Natural Language Processing (NLP) refers to the branch of artificial intelligence that gives machines the ability to read, understand, and derive meaning from human languages. NLP combines the field of linguistics and computer science to decipher language structure and guidelines to make models that can comprehend break down and separate significant details from text and speech. Every day humans interact with each other through public social media transferring vast quantities of freely available data to each other. This data is extremely useful in understanding human behavior and customer habits. Data analysts and machine learning experts utilize this data to give machines the ability to mimic human linguistic behavior. This helps save millions in terms of manpower and time as you don't need to always have a person present at the other end of the phone. We also use NLP every day in seemingly normal and insignificant situations. 

7 Steps to learn NLP

Break the entire document down into its constituent sentences. We can do this by segmenting the articles along their punctuations like full stops and commas for the algorithm to understand these sentences. 

For the algorithm to understand we get the words in a sentence and to explain them individually to our algorithm. So, we break down our sentences into their constituent words and store them. This is called Tokenizing where each word is called a token. 

We can make the learning process faster by getting rid of non-essential words that do not add much meaning to our statement and are just there to make our statement sound more cohesive. 

Now that we have a basic form of our document we need to explain it to our machines. First start off by explaining that some words like skipping, skips, and skipped are the same word with added prefixes and suffixes. This is called stemming 

We also identify the base words for different word tenses, moods, genders, etc… This is called limitization.

The concept of nouns, verbs, articles, and other parts of speech to the machine by adding these tags to our words this is called part of speech tagging

Next, we introduce our machine to pop culture references and everyday names by flagging names of movies, important personalities, locations, etc that may occur in the document this is called entity tagging. 

Once we have our base words and tags we use a machine learning algorithm to teach our humans sentiments and speech at the end of the day most of the techniques used in NLP are simple grammar techniques that we have been taught in school. With the increase of automated language solutions companies are looking for NLP experts to join them and are prepared to offer highly competitive salaries.  

Approaches of NLP applications 

  1. Sentiment analysis

  2. Named entity Recognition (NER) 

  3. Text Summarization 

  4. Language Translation

Sentiment Analysis:

Sentiment analysis is the process that automatically detects emotions and opinions by classifying the given text as positive, negative, or neutral. It combines natural language processing (NLP)  and machine learning. In today's world, we have overloaded data which does not mean better and deeper insights, it is still impossible to analyze it manually, without any error or bias.

What are the techniques used in sentiment analysis?

  1. Rules-based sentiment analysis 

  2. Machine learning 

Traditional Sentiment analysis works by using reference dictionaries of how positive certain words are, and then calculating the sentiments of that text.

Named entity Recognition (NER) 

NER is the form of NLP, it is one of the data preprocessing tasks. It involves the identification of the key information of the text and classifications into a set of pre-defined categories. An entity is the thing that is consistently talked about or referred to in the text. Some of the categories that are the most important architecture in NER are:

  1. Person 

  2. Organization 

  3. Place/ Location 

Deep learning NER is much more accurate than the previous method. It is capable of assembling words and understanding semantic and syntactic relationships between various words. It is also able to learn and analyze topic-specific high-level words automatically. This makes deep learning NER applicable for performing multiple tasks. 

Text Summarization

Text summarization in NLP is summarizing the information in large texts for quicker consumption. It is the approach that reduces the text size and creates a summary of our text data. There are 2 methods of text summarization:

  1. Extractive summarization 

  2. Abstractive summarization 

This is going to transform how we summarize massive volumes of information into relevant insights 

Language Translation

It is the process of translating text from one language to another without changing the meaning of the text. It is possible through algorithms and techniques like machine translation and natural language understanding. It can solve several real-world problems such as people from different countries and cultures communicating with each other, making international trade easier, improving education, improving health care, etc… With the help of machine learning and neural networks, it is becoming more and more accurate and efficient. 

Real-world cases of NLP

  1. Search engine results

  2. Language Translation 

  3. Autocomplete and autocorrect 

  4. Spell check 

  5. Email filters

  6. Chatbots

  7. Smart assistance (Siri, Alexa, or Google Assistance)

  8. Speech recognition 


Natural Language Processing (NLP) changed the way we interact with technology, enabling machines to understand, interpret, and produce more accurate and efficient human speech From sentiment analysis to named entity recognition to text summarization, and Language translation. NLP applications reshape the industry, inspire innovation, and enhance user experience through data quality, while challenges such as bias and privacy remain, the capabilities of NLP continue to expand. As businesses and organizations use NLP technology, the possibilities for enhancing productivity, decision-making, and communication are limitless. 

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