Natural Language Processing (NLP) is one of the trending technology which evolving deeply and widely in the market, irrespective of the industry and domains. It is extensively applied in daily life as well as in businesses. NLP is everywhere. We will learn about 10 Natural Language Processing Applications in our daily life as well as in business.
Some of the most important Natural Language Processing applications are:-
- Machine Translation
- Speech Recognition
- Sentiment Analysis
- Question Answering
- Automatic Summarization
- Chatbots
- Market Intelligence
- Text Classification
- Character Recognition
- Spell Checking
1.Machine Translation
As the total amount of resources available online is growing, the need to access it becomes increasingly important and the value of natural language processing applications and its use cases becomes clear. Machine translation helps humans to resolve the language barriers that humans often encounter by translating technical manuals, support content or catalogs at a significantly reduced cost. The main drawbacks or challenge with machine translation technologies is not in translating words, but in understanding the exact meaning of sentences to provide an actual translation.
2.Speech Recognition
The Natural Language Processing applications in speech recognition. Speech recognition has a lot of applications, such as mobile telephony, home automation, hands-free computing, virtual assistance, video games, and so on. Artificial Neural networks are widely used in this area.
3.Sentiment Analysis
Mostly used on the net & social media observation, natural language processing may be a great tool to grasp and analyze the responses to the business messages revealed on social media platforms. It helps to analyze the perspective and emotion of the author (person commenting/engaging with posts). This application is also known as opinion mining. It is enforced through a mix of natural language processing and statistics by distribution values to the text (positive, negative or neutral) and successively creating efforts to spot the underlying mood of the context (happy, sad, angry, annoyed, etc.)
4.Question Answering
As speech-understanding technology and voice-input applications improve, the need for Natural Language Processing will only increase. Question-Answering is becoming more and more popular thanks to applications such as Siri, OK Google, chat boxes and virtual assistants. A Question-Answering application is a system capable of coherently answering a human request.
5. Automatic Summarization
Information overload could be a real drawback once we ought to access a particular, vital piece of data from a large knowledge domain. Automatic summarization has relevancy not just for summarizing the that means of documents and knowledge, however conjointly for understanding the emotional meanings within the knowledge, like in collecting information from social media. Automatic summarization is very relevant once accustomed give an outline of a point or journal posts whereas avoiding redundancy from multiple sources and increasing the variety of content obtained.
6. Chatbots
We hear a lot about Chatbotsthese days, chatbots are the solution for consumer frustration regarding customer care call assistance. The company provides modern-day virtual assistance for simple problems of the customer and offloads low-priority, high turnover tasks that require no skill. Intelligent Chatbots are going to offer personalized assistance to the customer in the near future.
7. Market Intelligence
Natural Language Processing is a useful technology to track and monitor the market intelligence reports for and extract the necessary information for businesses to build new strategies. Widely used in financial marketing, Natural Language Processing gives exhaustive insights into employment changes and status of the market, tender delays, and closings, or extracting information from large repositories.
8. Text Classification
Text classification is an important part of several applications, like net looking, data filtering, language identification, readability assessment, and sentiment analysis. Neural networks are actively used for these tasks. Text classification makes us attainable to assign predefined classes to a document and organize it to assist you to discover the proper data you wish or alter some activities. For example, a use case of text categorization is spam filtering in email.
9. Character Recognition
Character Recognition systems also have numerous applications like receipt character recognition, invoice character recognition, check character recognition, legal billing document character recognition, and so on.
10. Spell Checking
Most text editors let users check if their text contains spelling mistakes. The concept behind this is Natural language Processing and neural networks.
