Natural Language Processing (NLP) is an emerging technology, a part of many types of AI, and it will continue to be a major priority for today's and tomorrow's more cognitive applications.
We'll go through some of the most useful NLP implementations in this article.
Uses of NLP
One of the most fundamental and essential applications of NLP online is email filtering. It began with spam filters, which identified specific words or phrases that indicate a spam message. But, like early NLP adaptations, filtering has been improved.
Gmail's email categorization is one of the more common, newer implementations of NLP. Based on the contents of emails, the algorithm determines whether they belong in one of three categories (main, social, or promotional).
This maintains your inbox manageable for all Gmail users, with critical, relevant emails you want to see and reply to fast.
Voice recognition allows smart assistants like Apple's Siri and Amazon's Alexa to identify patterns in speech, infer meaning, and offer a helpful answer.
We've grown accustomed to saying "Hey Siri" and asking a question, and Siri understanding what we're saying and responding with pertinent replies based on context.
And when we talk with Siri or Alexa through products like the thermostat, light switches, automobiles, and more, we're becoming accustomed to seeing them crop up across our house and everyday lives.
We now expect personal assistants like Alexa and Siri to interpret context hints as they enhance our lives and make certain tasks simpler, such as purchasing groceries, and we even like it when they reply with humour or answer questions about themselves.
(Suggested reading: Examples of NLP)
NLP is used by search engines to surface relevant results based on comparable search habits or user intent, allowing the ordinary person to discover what they're looking for without having to be a search-term wizard.
For example, when you type, Google not only anticipates what popular searches could apply to your inquiry, but it also considers the big picture and detects what you're trying to express rather than the precise search phrases.
Someone may type a flight number into Google to get flight status, type a ticker symbol to get stock information, or write a math equation into Google to get a calculator.
As NLP in search correlates the ambiguous question with a relative item and offers helpful results, you may witness some differences when finishing a search.
Search Autocorrect and Autocomplete
When you type 2-3 letters into Google to search for anything, it displays a list of probable search keywords. Alternatively, if you search for anything with mistakes, it corrects them for you while still returning relevant results. Isn't it incredible?
Everyone uses Google search autocorrect autocomplete on a regular basis but seldom gives it any thought. It's a fantastic illustration of how natural language processing is touching millions of people across the world, including you and me.
Both search autocomplete and autocorrect make it much easier to locate accurate results. Various other firms, such as Facebook and Quora, have since begun to use this functionality on their websites. (Here)
Machine Translation is the process of mechanically translating a text from one language to another while maintaining its meaning. Machine translation systems used to be dictionary-based and rule-based, and they were only somewhat successful.
Machine translation has become quite accurate in transforming text from one language to another, thanks to advancements in the science of neural networks, the availability of massive amounts of data, and powerful processors.
Today's tools, such as Google Translate, make it simple to transform text from one language to another. These technologies are assisting a large number of people and businesses in overcoming language barriers and achieving success.
Social Media Monitoring
People are increasingly using social media to express their opinions on a product, policy, or issue. These may offer important information about a person's preferences and dislikes.
As a result, studying this unstructured data can aid in the generation of useful insights. Here, too, Natural Language Processing comes to the rescue.
Companies now utilize a variety of NLP approaches to evaluate social media postings and learn what their consumers think about their products.
Companies also use social media monitoring to have a better understanding of the challenges and problems that their consumers are encountering as a result of utilising their goods. It is used by the government as well as businesses to identify possible dangers to national security.
Because people frequently utilise sarcasm and irony, interpreting natural language is particularly tough for robots when it comes to opinions.
Sentiment analysis, on the other hand, can detect minor distinctions in feelings and attitudes and identify whether they are good or negative.
You may monitor mentions on social media (and respond to unpleasant comments before they escalate), measure consumer reactions to your newest marketing campaign or product launch, and get a general idea of how people feel about your firm when you evaluate sentiment in real time.
You may also do sentiment analysis on a regular basis to learn what consumers like and hate about certain elements of your business.
For example, perhaps they love your new feature but are dissatisfied with your customer service. These insights can assist you in making better selections by revealing exactly what needs to be improved.
Text classification entails automatically comprehending, processing, and categorising unstructured text, and it also incorporates sentiment analysis.
Assume you wish to examine hundreds of open-ended replies from your most recent NPS survey. Manually doing it would take a long time and be prohibitively expensive. But what if you could train a natural language processing model to tag your data automatically in seconds, based on predetermined categories and your own criteria?
For NPS survey replies, you may utilize a topic classifier, which automatically tags your data by themes like Customer Support, Features, Ease of Use, and Pricing.
(Recommended reading: What is Text Mining?)
Text extraction, also known as information extraction, finds particular information in a text, such as people, corporations, locations, and more, automatically.
Entity recognition is another term for this. Keywords, as well as predetermined characteristics like product serial numbers and models, may be extracted from a document.
Text extraction may be used to scan through incoming support requests and extract particular information such as business names, order numbers, and email addresses without having to open and read each one.
For data input, you might alternatively utilise text extraction. You could extract the data you require and set up a trigger to insert it into your database automatically.
Keyword extraction, on the other hand, provides a summary of a text's substance, as demonstrated by this free natural language processing model.
When used in conjunction with sentiment analysis, keyword extraction may provide further information by revealing which terms consumers used most frequently to convey dissatisfaction with your product or service. (Here)
Question-answering is another important use of natural language processing (NLP). Search engines put the world's knowledge at our fingertips, yet they still fall short when it comes to addressing inquiries posed by people in their own language. Large technology corporations, such as Google, are also moving in this way.
Within the domains of AI and NLP, question-answering is a Computer Science topic. It focuses on developing systems that can automatically respond to inquiries asked by humans in their own language.
A computer system that understands natural language can use a software system to convert phrases typed by people into an internal representation, allowing the machine to create legitimate replies.
The precise answers can be found by analysing the questions' syntax and semantics. Some of the obstacles for NLP in developing an effective question answering system include lexical gaps, ambiguity, and multilingualism as explained by Tutorials Point.
(Must read: NLP guide for beginner)
Final thoughts!! These are the ten most practical uses of NLP, and they're just going to become bigger. Understanding NLP will undoubtedly be useful for you, given its development trajectory that has shown to be a lifesaver in this case.