What is the capability of NLP in 2020?
Tags: nlp, ai, datascience
Published: 2020-05-19 16:33:46

[Natural Language Processing](https://codegnan.com/nlp-course-online-training-with-python) or NLP, in short, is known for the capability to understand and interpret the human language in the manner it is written and spoken. The purpose of NLP in Computer Science is how computers are programmed to process and analyze large amounts of natural language data. For instance, when analysis of leads generated through Social media is done to understand what clients are saying and what they are looking for, it is the easiest example to understand NLP. Facebook and Twitter make use of NLP to track trending topics and popular hashtags.

Now, let us move forward to know what Natural Language Processing can do in 2020.  We’ll be discussing a few examples of NLP that people use every day:

Spell check
Autocomplete
Voice text messaging
Spam filters
Related keywords on search engines
Siri, Alexa, or Google Assistant

**Why do we need NLP in 2020?**

In 2020, as we are in the midst of the 4th industrial revolution, we need NLP for the following two reasons:-

**(I) Large volumes of textual data**

NLP makes it possible for computers to read the text, hear speech, interpret it, measure sentiment and determine which parts are important. The present machines can analyze more language-based data than humans, without exhaustion and in a steady, impartial way. Considering the amazing measure of unstructured data that is created each day, from records to social media, computerization will be basic to completely examine text and speech data efficiently.

**(II) Structuring a highly unstructured data source**

Human language is unpredictable and assorted. We communicate in boundless manners, both verbally and in writing. Not exclusively are there many human-understandable languages, yet inside every language is one of a kind arrangement of punctuation and linguistic structure rules, terms, and slang. At the point when we compose, we frequently spell or contract words incorrectly, or overlook accentuation. At the point when we talk, we have local accents, and we mutter, stammer and acquire terms from different dialects.

While supervised and unsupervised learning, and explicitly deep learning, are generally utilized for displaying human language. NLP is significant because it helps settle uncertainty in language and adds valuable numeric structure to the information for some downstream applications, for example, speech recognition or text analytics. 

**Read on the capabilities of NLP we can avail in 2020:-**

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/i/f91fqv78f1k63c2tohtm.png)

**NLP in the Healthcare Industry**

NLP with the collaboration of Machine Learning can be used in the healthcare industry to generate both structured and unstructured data. It can help to summarize the lengthy blocks of text, eg.  a clinical note by identifying the phrases in the source material. IBM Watson was used to researching how NLP and Machine Learning could be utilized to signal patients with heart diseases and assist clinicians to take the first aid. NLP algorithms were applied to understand patient data and a few hazard factors were consequently identified from the notes in the clinical records.

**NLP in Artificial Intelligence**

You might have seen the text translators or similar apps in your smartphones or simply you must have used Google Translate website on the web. Did you ever think how the human-written text is recognized and translated in the desired language in just a few seconds? Yes, this is because of Artificial Intelligence. NLP is used in data recognition and text conversion.

**To give you some notable examples:**

Google Translate goes through 100 billion words per day.

**Facebook** uses machine translation to translate text in posts and comments automatically, in order to break language barriers and allow people around the world to communicate with each other.

**eBay** uses Machine Translation tech to enable cross-border trade and connect buyers and sellers around the world.

**Microsoft** brings AI-powered translation to end-users and developers on Android, iOS, and Amazon Fire, whether or not they have access to the Internet.

**Systran** became the 1st software provider to launch a Neural Machine Translation engine in more than 30 languages back in 2016.

**NLP in Speech Recognition**

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/i/uquc006k6gxqbgmpfqxe.png)

NLP is helpful in speech recognition to allow users to dictate notes or other information that can be turned into the text. Think about the blind who cannot see the electronic device such as a phone or laptop. NLP, in that case, helps the person to write just using voice commands. Siri, Cortana, Google Assistant, Alexa, etc. are the family of speech recognition using Natural Language 
Processing frameworks.

**Computer-aided coding**

Another awesome capability of NLP adding significant detail and introducing specificity in the documentation. In your Computer Engineering, you might have used several IDE’s for coding purposes. In that, you must have seen when you start typing a keyword, it automatically completes the remaining keyword through it’s Artificial Intelligence. Or, another example is “I’m feeling lucky” feature of Google. It’s amazing how Google knows what we have in mind!

**Everyday Natural Language Processing**

There are numerous normal and functional uses of NLP in our regular daily existences. Talking with remote helpers like Alexa or Siri, here are a couple of more examples: 

Have you at any point taken a gander at the messages in your spam organizer and saw likenesses in the headlines? You're seeing Bayesian spam filtering, a factual NLP strategy that looks at the words in spam to legitimate messages to recognize garbage mail. 

Have you at any point missed a phone call and read the programmed transcript of the voicemail in your email inbox or smartphone application? That is speech-to-text conversion change, an NLP capacity. 

Have you at any point explored a website by utilizing its in-built search bar, or by choosing the recommended topic, entity or classification labels? At that point, you've utilized NLP strategies for search, topic modeling, entity extraction, and content categorization.

**NLP and text analytics**

Investigative discovery: Distinguish examples and signs in emails or composed reports to help recognize and solve violations. 

Subject-matter expertise: Arrange content into important subjects so you can make a move and find trends. 

Social media analytics: Track awareness and feeling about explicit points and 
recognize key influencers.

**Customer Service Automation**

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/i/pqomeb4sd42mbg35s9rf.png)

The above screenshot of client support automation given by DigitalGenius is somewhat unique in relation to the reply bot. It utilizes their restrictive NLP and AI to produce answers to generate answers and naturally fill case information. Those with certainty appraisals observed above—are automated, while the rest get sent to a human operator. This sort of automated help saves money for organizations. It additionally speeds up to help clients, who leave feeling more satisfied.

**Social Media Tracking**

Using various social media tracking tools comprehends what clients are stating via web-based networking media about a brand. NLP makes observations and reactions to that input. Using such tracking tools makes reference to the existent and nonexistent leads. With the use of hashtags, this becomes easier, which could be an indication to the organization the pitfalls and the strong points and the indication that something needs to change.

**Sentiment Analysis**
![Alt Text](https://dev-to-uploads.s3.amazonaws.com/i/d2928vsz161r2rocaqp7.png)

The classification of the sentiment of a text, comment, or article is a challenging task even for a real human. This is where NLP comes into the picture. A sentiment analysis model of NLP can tell us what kind of polarity a text has- very positive, positive, neutral, negative, very negative. You might be familiar with the below Facebook reactions!

With the use of sentiment analysis, we can classify things like the reviews of our company or its products. Another use of sentiment analysis is to poll people’s opinions based on their comments and social media posts.

**Recognition has started already!**

Natural Level Processing or NLP is well and truly placed to bridge the gap between the massive amount of data being generated on a daily basis and the limited cognitive capacity of the human mind. From the most cutting-edge applications to simple tasks, NLP has unlimited potential to turn data and processes from burden to boon. With the knowledge of NLP, you are futureproofed! hope you are clear with the topic now, and soon I will come up with a few more blogs on Artificial intelligence. Until then you can find an Artificial Intelligence blog post at [Codegnan.com](https://codegnan.com/)