Today we are here with Natural Language Processing. This is the ability of computers to human speech. By this, human speech is converted to computer languages like Java and Ruby.
With Natural Language Processing, the computer breaks down human voice according to accent. This is also the core technology of artificial intelligence. Tools like translation software, chatbots, spam filters, and voice assistance use this. Here we will cover every detail you need to know about NLP. So, let’s start.
Also Read: Everything You Need to Know Deep Learning
What is Natural Language Processing?
Let’s go through the definition first. Natural Language Processing (NLP) is a field of artificial intelligence. It stands for the capacity of a computer to understand human speech along with its accent. NLP is also the key to artificial intelligence and is based on machine learning.
Machine learning is a specific type of AI that analyses the patterns in data. However, it helps to improve the understanding of speech. NLP also combines the power of linguistics and computer science.
This is the process to study the rules and structure of the language. After that, computer software transforms this knowledge into machine learning algorithms. These rule-based algorithms can solve specific problems.
How does Natural Language Processing Work?
Natural Language Processing uses text vectorization. It changes the text into something that the machine understands. Then it helps to train the machine to make an association between input and output. After that, the machine uses statistical analysis methods.
It creates the own “knowledge bank”. Sentiment analysis also falls under the task of Natural Language Processing. However, NLP trains machine learning models to classify texts. Machines classify the texts based on the polarity of options.
What are The Uses of Natural Language Processing?
Natural Language Processing is part of artificial intelligence. So, it has many uses that we will discuss.
1. Sentence Segmentation, Part-of-Speech Tagging, and Parsing
The computer needs to break a sentence to understand it. In this task, NLP is an efficient tool. It is also necessary to understand the grammatical construction of the sentence.
2. Deep Analytics
Deep analytics involves the application of data processing techniques. It extracts specific information. Therefore, deep analytic extracts this information from multi-source data sets. It is also useful to deal with complex quarries with disorganized data.
The financial sectors, the scientific community, the pharmaceutical sector, and the biochemical industry are the users. Also, the organizations and companies are using deep analysis for business data mining.
3. Machine Translation
Natural Language Processing is good in machine translation. Machine translation is the translation of one human language into another one. The translation is also one of the essential parts of communication.
NLP breaks the sentence and translates the words. Then it reforms the sentence to present it in the translated form.
4. Named Entity Extraction
Named entity definition differentiates one item from the set of other items. It is a part of data mining. The goal of NEE is to make the mining process easier. The other set of items may have the same attribute, but NEE can define the targeted set.
Examples include first and last names, age, address, phone numbers, and email addresses. Named entity extraction is also known as named entity recognition.
5. Co-reference Resolution
In a chunk of text, there are some words to refer to something. The computer has to find those words to treat them. Also, there may be separate words to refer to a single object.
Here, co-reference comes into play. However, by this, you can identify those words that are in the text to refer to the same object.
6. Automatic Summarization
Natural Language Processing is an essential tool for summary production. Sometimes the computer needs to produce a readable summary from a large chunk of text. That summary should also contain the basic information and idea of that text.
For example, you can make a summary of an article with automatic summarization. The basic details of the content should be the same in that summary.
7. Creating Chatbots
Chatbots are assistants to help in customer handling. They collect and understand the quarries from the customers. Also, they provide suitable answers to their quarries.
So, these are the main working areas of Natural Language Processing. Now let’s look at the specific advantages of NLP.
Advantages of Natural Language Processing
There are some amazing benefits of Natural Language Processing. However, NLP can automate most of the tasks.
1. Automated Summarization
Companies are using NLP to improve the efficiency of documentation. It improves the accuracy of documentation. Also, it identifies the most prominent part from a large chunk of text. This is how it summarized a given content. It also saves the manual effort and eliminates the errors.
2. More Data Extraction Means More Understanding
By using NLP, enterprises get a better understanding of their customers. It helps to get customers’ habits, preferences, and tendencies. Companies can also use this data to make their products and services.
3. Fill The Communication Gap
When customers communicate with the helpdesk, they make a lot of mistakes. It is the responsibility of the business owners to fill those gaps. By using Natural Language Processing, they can also handle this task.
Also Read: Reinforcement Learning: All That You Need to Know About
Everyday Examples of Natural Language Processing
So, let’s discuss some of the real-life examples of NLP.
Email Filters: These are one of the most common and basic uses of NLP. These also help the businesses to send emails to their accurate receiver.
Virtual Help: Most common example is Apple’s Siri and Amazon’s Alexa. However, virtual assistances use NLP techniques to understand and process voice requests.
Predictive Text: When you start to type something on a smartphone, you will get some texting suggestions. Predictive text, autocorrect, and autocomplete, all these are an example of NLP.
Conclusion
We hope that you have got some points on Natural Language Processing. With advancing technology, NLP is becoming more accessible. If you want to get into this, today is the moment.
