Research/Blog
Natural Language Processing (NLP) and its Many Uses in Business
- July 31, 2020
- Posted by: CellStrat Editor
- Category: Artificial Intelligence Natural Language Processing
Survey forms, invoices, bills, memos, tenders, and myriad such documents which businesses face on daily basis. These are full of numbers, characters, images, signatures etc…in almost all languages worldwide…. hard to imagine our world without any languages.
Think about how many text and voice data we face every day. What about determining meaning from this data? Now we have systems that can-do additional functions with our language. These systems are based on NLP — Natural Language Processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyse large amounts of natural language data.
NLP is the machine’s ability to process what was said (speech recognition), structure the information received (natural language understanding and determining the necessary response) and respond in a language that we understand (natural language generation).
What can businesses expect from NLP? What tasks can be solved with NLP? The scope is great and every day the number of tasks is increasing. Here are some that come at top of my mind that are decently popular applications:
- Text generator
Earlier GPT2 and now latest GPT3 has made this problem disappear like magic. Many researchers have been trying to solve this problem for long but advances in NLP tech’s GPT model has made this a possibility. These models are trained on 4-6 Billion words due to which they are able to generate lot of very good text intelligently. I have personally used GPT2 recently to produce a complete chapter for my book on “Leveraging Artificial Intelligence for Business Success,” that got published yesterday.
- Machine Translation
We know what a manual translation is — we translate one language into another. When the same thing is done by a machine, we call it “Machine” Translation. The idea behind machine translation is — to train machine to allow automatic translation without any human intervention. The best-known application is Google Translate. This helps businesses in translating documents from foreign languages to their own language for ease of understanding.
- Speech recognition
For half a century, scientists have been solving this problem, and only in the last few decades, NLP allowed to achieve significant success. Now we have a whole variety of speech recognition software that allow us to decode the human voice. It is a mobile telephony, home automation, virtual assistance, video games, etc.
All-in-all, this technology is being used to replace other methods of input like typing, clicking, or selecting text in any other way. Today, speech recognition is a hot topic that is part of a large number of products, for example, voice assistants (Cortana, Google Assistant, Siri, …). Now, most businesses are trying to create apps interphases that include this technology for giving it a feel good factor of being voice enabled. E.g. how cool will it be to be able to create charts and graphs just by giving voice command to the machine instead of creating all by ourselves.
- Sentiment analysis
Sentiment analysis (also known as opinion mining) is a type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text. For example, this can be a movie review Why do we need this? Companies use sentiment analysis to keep abreast of their reputation.
Sentiment analysis helps to check whether customers are satisfied with their goods or services. Classical polls have long faded into the background. Even those who want to support brands or political candidates are not always ready to spend time filling out questionnaires. However, people willingly share their opinions on social networks. The search for negative texts and the identification of the main complaints significantly helps to change concepts, improve products and advertising, as well as reduce levels of dissatisfaction. In turn, explicit positive reviews increase ratings and demand.
- Question answering
Question answering (QA) is concerned with building systems that automatically answer questions posed by humans in a natural language. Some real examples of Question-Answering applications are: Siri, OK Google, chat boxes and virtual assistants — all of them have a few NLP-applications or functions — to understand speech is only half of the path and another one naturally is to give a response.
- Automatic summarization
It is a process of creating short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the reading time. Some of the APIs one can try are: ML Analyzer, Summarize Text, Text Summary etc.
- Chatbots
NLP has become the basis for creating chatbots which can easily handle standard tasks. Chatbots currently operate on several channels, including the Internet, applications, and messaging platforms. Businesses today are developing bots that can not only understand a person but also communicate with him at various levels automating some of the most mundane and repetitive tasks, most being used for 24/7/365 customer service. Both voice and chat bots have seen heavy deployments in all sorts of industries during this pandemic.
- Market Intelligence
Marketers use NLP to search for people with a likely or explicit intention to make a purchase. Behaviour on the Internet, maintaining pages on social networks and queries to search engines provide a lot of useful unstructured customer data. Selling the right ad for internet users allows Google to make the most of its revenue. Advertisers pay Google every time a visitor clicks on an ad. A click can cost anywhere from a few cents to more than $ 50.
Market Intelligence uses multiple sources of information to create a broad picture of the company’s existing market, customers, problems, competition, and growth potential for new products and services. Sources of raw data for that analysis include sales logs, surveys, and social media, among many others.
- Text Classification
Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. What is it? Suppose you distribute documents in certain categories. A new document arrives, and it is necessary to determine, to which category, it belongs. By using NLP, text classifiers can automatically analyse text and then assign a set of pre-defined tags or categories based on its content.
- Character recognition
Character Recognition systems has numerous applications like receipt character recognition, invoice character recognition, check character recognition, legal billing document character recognition etc.
- Spell Checking
A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Most text editors let users check if their text contains spelling mistakes. One of the most vivid examples is the Grammarly app. It is an online grammar checker that scans your text for all types of mistakes, from typos to sentence structure problems and beyond.
- Text to speech for blind people aid
Most blind people suffer from the problem of getting lost whenever they change their route or want to know some information but cannot read. In such cases, apps that convert text to speech come to rescue for these people.
- Automated essay scoring
It is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades.
- Advertising copy generator
Successful ad copy writing is not everybody’s cup of tea. It requires lot of practice and industry experience. However, now it is possible for everybody who can deploy NLP based ad copy generators. These machines are trained on large data sets of words and various kinds of ads and ad copies of all kinds of ads across the world along with possible success attributes of good ads. So large companies and ad agencies employ these kinds of machines along with their ad copy writers for fast and successful ads generation.
- Speech to text generator for medical transcription
Earlier this was done by humans and hence was slow and tedious process. But now it has been fastened up with the application of NLP technologies which has converted speech to text generation task – which used to be work of days into a task which gets done just in a few hours.
- Native Language Identification (NLI)
NLI is the task of determining an author’s native language based only on their writings in a second language. It finds its applications in second-language acquisition, language teaching and forensic linguistics, etc.
- Optical character recognition (OCR)
OCR helps in recognition of characters in images. Thus, most financial related documents are read and converted to digital forms using OCR software and hence are very popular among digital transformation companies.