What is Natural Language Processing - NLP Uses and Tools

What is Natural Language Processing - NLP Uses and Tools

Natural Language Processing is a specialization of Artificial Intelligence. If we closely observe, fundamentally, every human interaction happens through language. Natural Language Processing assists in providing a machine with the ability to comprehend languages (texts and speech) the same way a human would. Voice assistants, machine translation, etc., are ubiquitous in the digital world today. These are a few of the standard applications of Natural Language processing. In this article, you will be exploring the nitty gritty of NLP from the basics- its working, applications, challenges, and solutions.

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What is Natural Language Processing (NLP)?


NLP is that branch of Computer Science that bridges the gap between Human interaction and Machine by comprehending languages as texts, speech and more. More specifically, this study is a subfield of Artificial Intelligence. In today’s world, there is a growing need for automation as it saves time and promises accuracy. NLP blends computer science and machine learning with linguistics to ease communication. NLP enables machines to interpret results the same as human beings. Thus, bridging the gap between human-machine exchange.I.E. making machine interaction understandable for human interaction.

How does NLP work?

Natural Language Processing works in three different stages:

Stage 1. Speech Recognition

You may call the first stage the input stage. It involves speech recognition. The machine (an electronic device or a computer) will receive speech instructions, which will be interpreted into text. A relatively prevalent example would be voice assistants like Alexa or Siri. All you have to do is provide the instructions as you speak. The devices then process it and use speech recognition to convert it into chunks of text to interpret the result. The Hidden Markov Model (HMM) is a popular model for speech-to-text conversion purposes. The model involves mathematical calculations that interpret your speech and transform it into text.

Hidden Markov Model

The model examines your voice input and breaks it into small fragments or units (up to 10 to 20 milliseconds). After that, the programs implement statistical analysis to find the closest word.

Stage 2. Natural Language Understanding

As the name suggests, the next step involves the understanding part. The primary goal of NLU is to process and interpret the speech word by word to bring out the most accurate output. NLU involves AI methods to understand each word along with the sentiment of the speech and the tonality of the sentence. A set of grammar and vocabulary is built in NLP systems. This assists in getting the closest results. This stage is the most crucial and challenging part at the same time. It involves speech interpretation. Now, what if languages have words with multiple meanings? How does the machine discern the difference and when to use each? For instance, consider the word "tank." The tank may mean a water tank. It may also mean a storage chamber for military purposes or even a drivable artillery machine. Besides, a sentence can have different tonality. This is where high-ended technical resolution comes into play. Developers provide different versions of technical solutions to make the machine come up with the closest possible solution. NLU may be seemingly an easy process. After all, it involves just the tokenization of instruction and interpretation. Conversely, it belongs to the category of AI hard tasks. As much as it seems easy, it is not. It involves text recognition and sentiment analysis. Now both of these factors (text and sentiments) may vary. That implies it may form different meanings for the same sentence or word. Therefore this is the hardest step of the NLP work.

Stage 3. Natural Language Generation

Here we are at the final stage. NLG produces the final text and converts it into speech. Natural Language Generation is a relatively easy step. It aims to deliver thoughtful and conversational (it must resonate with the user) output.

It involves three stages of its own:
  • It interprets the database to produce a text.
  • Then it builds a sentence.
  • Finally, NLG structures the response that resonates with the user.

Let’s take an example. Here’s a situation. You are looking for a nearby shopping mall in your area. If your device gives you a list of the nearby eateries, then clearly, it would be an incorrect answer. That’s where NLG comes into play. It comprehends your input and processes it into a relevant answer that can assist you. That’s how you finally listen to the output- a machine responding to you!

What are some of the real-world cases of the NLP being used today?

Natural Language Processing is now an essential component of the digital revolution. It has made human-machine interaction a reality. Some of the significant applications of NLP are listed below.

Uses for Natural Language Processing

1. Speech Recognition

It is one of the most widely used applications of NLP. Siri or Google Assistant are prevalent examples. NLP processes each word in the form of tokens. It analyses each token to produce the final result. In today's digital world, anything is possible at the command of your instructions to a machine. This is where NLP assists you in making it happen.

2. Sentimental Analysis

In today's fast-paced world, most of our communication takes place on our phones via social media applications, video apps, and other apps. Sentiment analysis is the process through which NLP technology can recognize the behavior of a certain text in the form of posts. For example, if someone tweets their views on Twitter, NLP can detect its sentiment as "good," "bad," "pleasant," "outrageous," etc., which can further help in understanding the behavior Another example would be that during an election or promotion, leaders and influencers post their views and concerns. Their language use can be used to analyze their behavior. Thus, sentiment analysis is crucial in determining how the public feels about a topic that may affect them in the future.

3. Machine Translation (When the Machine becomes an interpreter)

This is another typical application for NLP. While interacting with your devices, have you ever come across different languages that you could not understand?Machine translation conveys the message in a different language (speech or text) in your language quickly. For example, Google Translate. Once again, the accuracy level is high when compared to the human interpreter.

4. Chatbots

Intelligent chatbots allow machines to interact and communicate in predictable ways. Machines usually have a stack of databases. Out of that, it filters the one which aligns with your query. Thus, it makes the process of customer assistance through chatbox (for customer resolution) far simpler,  more automated and more effective than human interactions. It also weighs out the extra back and forth in the initial stages of a query. A problem is escalated only when it becomes unsolvable. Thus, it saves time and makes the process easier. A common example would be Amazon. When you are facing an issue, you click on chat assistance. Herein, you don’t talk to a human but a chatbot. It offers you possible solutions based on your response to the initial questions. In this way, they save so much time because most questions are resolved halfway through the bots’ reply itself. In some cases, the concerns get resolved through the bot’s suggestion without the need for a representative. Therefore, it significantly reduces the response time (to the query).

Challenges in Natural Language Processing

As with any new and emerging technology, there are bound to be setbacks along the way. The following is a list of some of the challenges faced in the Natural Language Processing process:

1. Lexical Ambiguity

NLP involves the tokenization of words. One word may have multiple meanings. Thus, there are chances that the users’ instructions may not be comprehended well.

2. Syntactical Ambiguity

The structure of the inputted sentence must be accurate and clear.

Example: The young boys and girls were taken to Wonder Land. Now for humans, it is easy to imply that both boys and girls are young. However, the machine may or may not comprehend girls as young there is no adjective in front of the “girls”. So a clearer version would be “The young boys and the young girls were taken to Wonder Land.” It is because a machine breaks each word into tokens. So for clarity, it is better to set the context of the sentence.

3. Semantic Ambiguity

Semantic Ambiguity may arise due to contextual confusion. Something may be understandable in human interaction. However, a machine needs to comprehend each word with a clear context. Example: "Reena crashed into a car while running. "For a machine, it is ambiguous to find out who was running after all. The car or Reena?Thus, this sentence becomes ambiguous. The process employs Lemmatization ( a procedure which converts any word into its fundamental form) to curb this problem. Lemmatization involves understanding the context of the subject thereby curbing semantic ambiguity. So, it is usually solved by providing multiple meanings to a phrase. It also involves clear distinction amongst verbs, nouns, adverbs and more.

4. Pragmatic Ambiguity

Pragmatic ambiguity occurs when the sentence has multiple meanings. It usually happens in open-ended lines.

Example: “My friend is coming". This phrase can have multiple meanings-

  • where is the friend coming form,
  • to whom will she meets, etc.

In our daily “human” interactions, it may be understandable. However, a machine needs to know each word, and context line by line, word by word. Nevertheless, today, equally advanced solutions are coming up to resolve these burning ambiguities systematically.


Natural Processing Language (NPL) is rapidly changing the present and will surely advance the future. It is the backbone of the most significant digital developments in the world today.  Voice assistants, chatbots, etc. have effortlessly snuggled into our devices. NLP is the key player behind this. It has made machine-human interaction an ever-increasing norm.

Stay tuned to https://www.rapidinnovation.io/ for more technical dives! Note.

About The Author

Jesse Anglen, Co-Founder and CEO Rapid Innovation
Jesse Anglen
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