Natural Language Processing in Artificial Intelligence

Natural Language Learning Processing

Not long ago, someone dreamt of developing a robot. Especially something capable of reading and interpreting human speech and writing. People desired one that could do the same with their voices.</p>
But it was something you’d see in a science fiction book or something you’d see in a movie. Guess what! This idea, known as Natural Language Processing is now a reality and ready to serve.

With the evolution of computers and technology, the idea of NLP grew out of linguistics. This post will explain what natural language processing is and why it is vital for us.
You will eventually learn

•The evolution of NLP
•How does it work
•The real-life application of NLP.

Evolution of Natural Language Processing

The journey of NLP began in the 1950s with Machine Translation(MT). Early MT attempts helped decoders throughout WWII. Though the early MT phases failed, they paved the way for more complex technology.
The early 1960 saw NLP creations like ELIZA and SHRDLU. With ELIZA (the first chatbot), a user could have limited psychological interaction. NLP used tricky rules and parameters until 1980.

The late 1980s saw the rise of NLP. Especially Statistical NLP and machine learning-driven language processing evolved in this period. Thus it fueled modern NLP.

With the current array of technologies, NLP has come a long way from MT. Currently, 47.3 million Americans own a smart speaker with NLP. Chat bots are another popular NLP application.

A recent Oracle poll found that 80% of respondents use or plan to use chatbots for consumer products by 2020. So experts predict its market to hit USD 16.07 billion by 2021, growing 16.1% per year.
The market is booming as businesses worldwide are adopting NLP solutions. As a result, bridging the human-machine divide ensured.

What is Natural Language Processing?

Natural language processing (NLP) is a subject matter of artificial intelligence systems (AI). It integrates statistical, machine learning, and deep learning models. These help computers understand natural human language (intent and sentiment).
Computer software can use NLP for many purposes. Such as fast translating, replying, and summarizing bulk information. Not surprising if you’ve unknowingly used NLP. Siri, Google Assistant, and customer service chat bots are all exam plus of it.

Many enterprise solutions rely on NLP to improve productivity and automate business processes. Such as sentiment analysis. It employs this natural language system to assess the emotional content of the text. It is a popular NLP task for detecting brand sentiment on social media. Firms can use it to detect critical consumer issues and track customer satisfaction.

To do all these tasks, NLP uses the tools below.

Python and the Natural Language Toolkit (NLTK) Language Model in Python offers various tools and libraries for various tasks. Libraries include such as lemmatization and semantic reasoning. This toolkit is also used in computer-vision systems.
Statistical NLP
Statistical NLP uses machine learning and deep learning models. They extract, classify, and label raw data ( text and voice). For that reason, some of the earliest NLP applications were hand-coded rules-based systems.

Why is Natural Language Processing important?

The improved natural language processing techniques have a lot of benefits. It allows professionals to save more than just time. From improved communication to the identification of critical data. You name it! Some noteworthy among them are such as;
It facilitates the analysis of large amounts of natural language
• data. Such as social media comments, tech support tickets, user reviews, and news reports. Thus all this data offers a lot of great insight.

• NLP helps machines understand human language better. So it can help fast in identifying those insights.

• Natural Language Processing tools process data in real-time, 24 hours a day. Moreover, it keeps on applying the same algorithm to all your data. As a result, it ensures accurate and consistent results.

Once NLP tools understand a text’s content, they start quantifying sentiment. So organizations can start prioritizing and coordinating their data following their specific requirements.

Who is using Natural Language Processing?

Earlier, I have discussed the use of Natural Language Processing applications. Such as chat bots, surveys, and social media monitoring. Other notable NLP users include:

People like you and I rely on Google’s autocomplete and autocorrect features. Besides Facebook, Quora has started employing this feature on their websites. Everyone utilizes it daily yet rarely notices it.

Email messaging apps filter mails using text classification, a natural language processing method. For instance, you might have noticed the classification as primary, social, or promotional. The best part is filtering Spam emails.

Tools like ‘Grammarly’ have many features that help writers improve their content. Especially writing an email to your boss or a report, or even an article requires this helpful friend.Besides this tool helps improve content clarity and engagement.

Retails that suggest things based on prior purchases using machine learning. Thus they check your purchasing record. Usually, retailers rely on machine learning to gather and customize the shopping experiences. Also, machine learning needs for launching promotional campaigns. All these are the basis of computer vision in this modern world.

On-line activity shows the user-targeted ads. Likewise, Google ads often contain your search term, as you may have noticed. This works through keyword matching. In other words, the ads are only shown to users who search for that keyword or phrase.

How Does Natural Language Processing Work?

Like humans, computers have systems to read and microphones to capture audio. Also, computers have a program to process their inputs.
During processing, the input turns into computer-readable code

There are two steps in natural language processing;
•Data preprocessing.
•Algorithm development.

Data Preprocessing
Preprocessing text data prepares it for machine analysis. For that reason, it cleans up data and emphasizes text features that an algorithm can use.
Preprocessing methods are:
Tokenization: Tokenization divides the text into smaller parts. Then it deletes common terms from text. That’s how it leaves unique words that convey the main information.
Lemmatization and stemming: Lemmatization and stemming turn words into their main forms for processing.
Part-of-speech tagging: It marks words as nouns, verbs, or adjectives, depending on their part of speech. After that, the method creates an algorithm to process the data.
Algorithm development:
The two most prevalent types of NLP algorithms are:
Rules-based Approach: Rules-based system employs linguistic rules. Developers has been using it since the early NLP development.
Machine learning Approach: This method uses Statistical methods. They adapt their algorithms as it analyzes more data. and they learn to do tasks depending on the training data. Yet, the algorithms keep on refining their rules. For that, it uses a mix of machine learning, deep learning, and deep neural networks.

Now that you know the applications, you may dig into the realm of Natural Language Processing. Please use the comment area below to tell us about any other excellent NLP applications you may know.

I hope you found this article interesting. Please share this post with your friends and leave your thoughts and questions below. Also, let us know about any fantastic NLP applications we missed.