People don’t often think about the ambiguities that come with human language. They use language as an intuitive behavior to pass information using semantic cues such as signs, images and words. It is the easiest thing to learn since it comes more naturally as one grows; it can be trained and is repeatable. However, what comes naturally to human beings is certainly difficult for computers to master, considering a large amount of unstructured data, absence of intent and lack of formal rules.
Because of that, Artificial Intelligence (AI) and Machine Learning (ML) are gaining popularity, momentum and attention from organizations. Together with great human intervention, AI and ML help computer systems perform certain tasks and, more importantly, communicate. As AI develops, so does Natural Language Processing (NLP).
This article will help you learn more about NLP, its challenges and its applications in business. But first, let’s start by defining Natural Language Processing.
What is NLP?
Natural Language Processing helps machines and computers to interpret and break down human language. It is among the most important tool we use daily, ranging from spam filters, translation software, chatbots and social media monitoring software to search engines, voice assistants and grammar correction software. Therefore, NLP is an AI field that helps machines understand and interpret the human language or makes the human language easy to read by machines.
Natural Language Processing uses both computer science and linguistics to bring understanding to human language structure and rules. This results in an intelligent system that runs on NLP and ML algorithms capable of analyzing, understanding and extracting meaning from speech or text.
Benefits of Natural Language Processing
NLP offers many benefits to organizations that take it seriously. Below are some top-level advantages your business will gain to be more competitive.
Process Automation in Real-time
The good thing about Natural Language Processing tools is that is can help machines work (sorting and routing information) with minimal human interaction. The process will be accurate, quicker and efficient.
Large-Scale Analysis
With the help of NLP, machines can automatically analyze and understand large unstructured data such as customer support tickets, news reports, social media comments and online reviews.
Tailor Natural Language Processing Tools Based on your Organization
It is easy to tailor Natural Language Processing to the specifics of your company or industry. Therefore, you can tailor NLP to your criteria and needs, like your industry-specific language, where it can flag misused words or sarcasm.
Natural Language Processing Examples
When you hear of AI or NLP, your mind may go straight to robots. That is not the case here; below are perfect examples of Natural Language Processing in day-to-day life.
Search Results
NLP plays an important role in the search engine interface in that it helps generate relevant search results based on user intent or similar search behavior. Therefore, people can find what they are looking for without using critical or specific terms. A good example is the google search engine. NLP tools in the google search engine will predict what you are trying to search the moment you start typing.
Also, it will link similar and popular searches to your question to get what you are trying to say rather than the exact meaning of words. You may see several variations when searching for something using an NLP-supported search engine. Natural Language Processing will associate any wrongly-written or ambiguous text to a relevant entry and offer useful search results.
Text Analytics
Text analytics uses different statistical, linguistic and ML techniques to convert unstructured data into easy-to-read and meaningful information suitable for analysis. Sentiment analysis is a difficult task for many organizations, but a tool with Natural Language Processing can boost customer interaction and make things easy for the customer support team. Analysis of customer interactions, like reviews or social media comments, help brands to monitor common customer issues or know how a certain campaign is fairing. NLP also helps with keyword extraction and finding patterns or structures in a given unstructured data.
Smart Assistance
NLP has given birth to many smart assistants like Alexa and Siri, making our lives easier. They work by recognizing speech patterns using voice recognition, then attach meaning to words and give out useful responses. Smart assistants will wait for your question, understand it and respond with an answer based on your question and context.
Now, smart assistants are almost everywhere, from light switches and thermostats to cars and computers. These assistants are expected to understand the context of our speech and respond accordingly, thus making our lives easier.
Language Translation
NLP has made it possible to translate content from one language to another more accurately and with correct grammar. Now it is easy to communicate using different languages. NLP tools have improved in that they can recognize a certain language based on the text input.
Predictive Text
There are a lot of things in our computers and phones that we take for granted, not knowing their importance and contribution. Predictive text and autocomplete work similarly to search engines (they predict words based on your typing, suggesting relevant words or finishing the word).
Predictive text will personalize itself and all your activities the more you use it. You can make a complete sentence made up of predictive text on your computer or phone.
Stages Involved in Natural Language Processing
Natural Language Processing is trying to make computers intelligent, making them act like humans and making humans believe they are communicating with fellow humans. The whole process can be divided into three main parts.
The first and most important part is for Natural Language Processing tools to understand the language input (the natural language the computer receives). The built-in computer statistical model will carry out speech recognition exercises to convert natural to programming language. This is done by breaking down current speech into small units and compare with the previous speech. This process is called speech-to-action.
The second step is termed word-category disambiguation or POS (Part-of-Speech) tagging. This step works by identifying words in their correct and grammatical form, like adjectives, nouns, future tense and verbs. After these steps, the computer has a clear idea of the meaning of the text or voice. The process uses lexicon rules set and coded into the software.
Text-to-speech conversion is the last step taken by NLP. The user at this stage could have converted the computer programming language to either textual or audible format. You will get your answer based on your search history or prediction made by NLP tools.
Take Away
Natural Language Processing has proven to be part and parcel of our daily life thanks to its many application in the digital world. The list will grow longer and longer as organizations, industries and businesses learn to embrace the value NLP brings. Although the human touch is essential in most communication issues, Natural Language Processing can make life simpler by automating and managing smaller and more complex tasks with technological innovation.
Tags: Artificial Intelligence, Automation, Machine Learning, NLP
