- 1 Introduction
- 2 Application of Natural Language Processing
- 2.1 Text Classification or Categorization
- 2.2 Sentiment Analysis
- 2.3 ChatBot
- 2.4 Automatic text generation
- 2.5 Document Summarization
- 2.6 Character Recognition
- 2.7 Speech Recognition – Virtual Smart Assistants
- 2.8 Question Answering System
- 2.9 Named Entity Recognition
- 2.10 Part of Speech Tagging
- 2.11 Paraphrase Detection
- 2.12 Machine Translation
- 2.13 Document Search Engine – Autocomplete | Autocorrect | Smart Search
- 2.14 Sign Language to Text
- 2.15 Spell Check
- 3 Conclusion
Artificial Intelligence has multiple sub-fields, one of them is Natural Language Processing (NLP) that gives computers human-like cognition to understand natural language In this article, we’ll learn about real-world uses and applications of Natural Language Processing. This will help us in understanding how NLP is actually used in collaboration with different fields and what all novel features we can achieve with the help of such modern-day technologies. So let’s start this article and learn about the applications in a detailed manner.
Application of Natural Language Processing
1Text Classification or Categorization
Text Classification or Text Categorization is a way to perform the labeling of natural language texts into a predefined set of relevant categories. The underlying purpose is to categorize the information from a large chunk of text data. This NLP application has enhanced the searchability of various systems.
Tagging of contents on e-commerce, news apps, blogs, and social media apps is one of its use cases. Along with this, various organizations are using this technique to manage unstructured text. Lionbridge.ai is one of those companies that prominently focus on providing text classification services.
Sentiment Analysis is one of the most well-known applications of natural language processing that understands and classify sentiments or emotions from text data with the help of various NLP techniques. Sentiment Analysis is generally used by companies to summarize feedback from customers about products, movies, brands, and different online movements. It can also help in understanding people’s thoughts on their government and its policies. Some well-known companies using Sentiment Analysis are Scale AI, Lexalytics, and Monkey Learn.
These are bots that are capable of striking a conversation with people in natural language. Sophisticated chatbots can maintain the context of the conversation using NLP and artificial intelligence. Mostly, chatbots are incorporated into messaging applications, websites, and sometimes even in telephonic based customer support systems.
For e.g. Banks are extensively using chatbots for automated customer interaction to full advantage.
4Automatic text generation
The automatic text generation application is used for generating textual data that can be used by language models to predict the occurrence of words based on the words already present in the text. Therefore the language models are trained with such kind of data and then prediction takes place for generating text. OpenAI has built an AI-based text generator which has gained a lot of popularity from people.
Text Summarization or Document Summarization is a technique through which we can automatically generate concise summaries of large pieces of text. There are situations where we need to understand the gist of text available and this can be done efficiently through document summarization. SummarizeBot is one of the companies that have leveraged the power of text summarization.
Agolo, an AI startup is working in this domain with support from tech giants like Google and Microsoft.
The character recognition is another useful application of Natural Language Processing through which we can perform the recognition of characters present in a given text. This character recognition helps to automatically identify text characters on receipts, invoices, cheques, and legal bills. The latest NLP based models are even able to recognize handwritten characters with great accuracy.
Dropbox has developed such OCR models where character recognition can be performed on stored images and pdfs.
7Speech Recognition – Virtual Smart Assistants
We, humans, have the unique ability to express our thoughts in the external world through speech. Speech recognition is one of the ways through which machines are made capable of understanding the human natural language.
Smart Virtual Assistant is the major innovation that has been made possible due to speech recognition. Apple’s Siri, Google’s Voice Assistant, and Amazon’s Alexa are some of the well-known voice assistants that are able to understand our voice commands for daily tasks with ease.
Nuance is a well-known company that has built a virtual assistant named Nina. Its performance has been commendable and is attracting more companies for availing of the services.
8Question Answering System
The question answering system is an information retrieval tool that leverages Natural language processing. These kinds of QnA systems help in answering questions put up by humans in their natural language. By fusing multiple applications of NLP, a competent QnA system is built.
The customer support system is highly benefitted with the advent of question answering systems. Customers can look for the answers to their regular queries from such QnA systems.
9Named Entity Recognition
The named entity recognition is a process that takes input in the form sentences or paragraphs. This input is then used for finding all the nouns present in it and further these extracted nouns are categorized into predefined classes.
For example, consider this sentence – Tom went to Paris for his work
NER will produce the following output – Tom [Person] went to Paris [Place] for his work
NER technique is used by news providers for classifying content, building robust search algorithms, powering recommendation systems, and empowering the customer feedback system.
10Part of Speech Tagging
Part of speech tagging, also known as grammatical tagging, is a process that automatically tags a word in a corpus of text with a particular part of speech tag on the basis of context and definition. POS tagging is used in NLP applications like Text to Speech Conversion, and Word Sense Disambiguation.
Paraphrase detection is helpful in deciphering if two different sentences have similar meanings or not. This kind of application is generally used in online discussion forums, question answering systems.
This is one of the best applications of Natural Language Processing that has helped in removing the language barrier present between people of different countries. With globalization, people from different countries are collaborating together for business purposes. Now employees can work with guide manuals of foreign languages. Travelers are now able to manage their world exploration through machine translation.
Google translate has been a pioneer in machine translation, supporting over 100 languages spoken on this globe. Lilt and PangeaMT are two emerging startups that are leading this NLP enabled machine translation venture.
13Document Search Engine – Autocomplete | Autocorrect | Smart Search
Due to the explosion in big data generation, a huge amount of unstructured data is saved in documents by various organizations in NoSQL databases. The tedious task of accessing documents from humongous databases is simplified with the help of document search engine application which is built with the help of natural language processing.
The search capability is further boosted through other NLP applications like search autocomplete, search autocorrection, and smart search. These applications are improving the document search engine and product search in various e-commerce companies.
14Sign Language to Text
Another highly beneficial and novel application of natural language processing is the conversion of sign language into text. This application is especially for those individuals who possess hearing impairment. A Budapest based company SignAll is working in this direction where people with the inability of hearing can communicate through sign language with those individuals who are not aware of sign language.
Spell checking is one of those features that we interact with, in our day-to-day activities. Whether it be text editors or different forms that are filled for various services, all of these require a really good spell checking system. With the introduction of natural language processing, spell-checking tools are getting enhanced with each passing day. Mainly neural networks are fused with NLP techniques to improve the performance of spell checkers in text editors.
Salesforce is one of those companies that leverages the power of NLP-based spellchecking in the forms used by their customers. This implementation has reduced the stress of salesforce employees who get annoyed due to spelling blunders leading to miscommunication.
We have reached to end of this informative article where we looked at the real-world applications of natural language processing in various domains. We also learned about the different companies that are incorporating NLP in their products, thus enhancing the services provided to the customers.