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Natural Language Processing – An Overview

Natural Language Processing, NLP, NLU, NLG, Machine Learning, Artificial Intelligence, AI, ML

What is Natural Language Processing?

Artificial Intelligence (AI) has boomed in the Global Market by lending a helping hand to businesses in their omnidirectional growth. Natural Language Processing (NLP) is a key element of AI that helps the computer in analyzing and processing human languages.

NLP is used in various key processes including Intelligent Document Analysis (IDA) wherein it derives insights from unstructured data like messages and tweets, Conversational AI wherein it analyses human language by breaking it down, Text Analytics wherein it transforms unstructured data into documents and databases, Voice Of Customer (VoC) analysis wherein NLP helps in understanding the opinions of customers about a product/service etc.

Natural Language Processing is broadly divided into 2 processes: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The former is the analytical branch of NLP used by the computer to get a contextual insight of the text, whereas the latter is useful for enhancing the computer’s capability to generate a response by processing the retrieved information and forming a meaningful sentence.

Stages in Natural Language Processing:

NLP algorithms work through the audio/textual data in stages which include Lexical Analysis (Breaking sentences to tokens of meaningful words), Syntactic Analysis (Grammatical analysis stage), Semantic Analysis (Deriving the dictionary meaning), Discourse Integration (Analysing the meaning of the previous and the next sentence to derive context), Pragmatic Analysis (Final re-interpretation), and finally, Chunking (Grouping the segmented words into sentences and paragraphs).

Challenges in NLP

A computer’s understanding of the human using NLP would always be limited to the vocabulary of the group of programmers developing the algorithm. But human language has a lot of irregularities, ambiguities, alternate uses, and dialects. While humans can perceive the context and intention, it becomes difficult for a machine to understand why the text made out of the same bag of words, conveys a different message. A solution to this is providing the machine with more and more data, because the more it “learns”, the better NLU becomes.

Applications of NLP include:

While NLP is a simple process, the versatility of its applications is noteworthy:

Text Mining:

Text Mining includes ETL (Extract, transform, load). Extracting useful data from silos of unstructured data present on the internet and documenting it into databases is one of the most useful applications of NLP. This data is then fed to ML algorithms for processing. It can be helpful in risk management, monitoring emails, cybercrime prevention, and customer care service, etc.

Sentiment analysis:

NLP tools are leveraged to understand customer opinions by processing data like customer feedback, reviews, and reactions. This data then helps the organization to improve by checking whether the users are happy or sad or have neutral opinions about the current progress in the brand.

Target Advertising:

As the name suggests, a target audience is selected for advertising the organization’s brand based on their online activity. The NLP tool used here is Keyword Matching. This helps the organization save a lot of money by only entertaining the potential audience.

Email Categorization:

By evaluating the type of content in each email, with the help of NLP, emails that we receive are divided into categories namely Promotional, Social and Personal. Apart from this, NLP also finds its application in spam filtering, where NLP filters out emails having a specific recurring pattern of irrelevant data into the spam folder and email evaluation to check if any malpractices are happening in the organization.

Automatic summarization:

While NLP is known to help with autocorrect and word suggestions, it also helps in summarizing lengthy documents into short, meaningful reports. A deep learning model developed using NLP can extract keywords in the text to combine them into a fluent and meaningful report.

Competitive Analysis:

There a lot of NLP-powered tools that help with the research and competitive analysis for a company. This includes ranking the companies so that the company’s growth can be monitored and its position in the market is administered.


NLP, which is powered by Machine Learning, is a stepping stone in the vast network of AI. It has end-user-specific applications. As you learn more and more about the applications of NLP, you can automate more and more sectors in your workspace, thus resulting in an efficient, effective, and profitable workspace.