Natural Language Processing is a branch of artificial intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages.
A variety of statistical techniques are used in machine learning for NLP and text analytics to recognize entities, sentiments, portions of speech, and other properties of text. The methods can be encapsulated in supervised machine learning, often known as a model that is then applied to further text. Unsupervised machine learning is a term used to describe a group of algorithms that operate on huge data sets to extract meaning. Understanding the distinction between supervised and unsupervised learning, as well as how to combine the finest features of each, is crucial.
EXAMPLES OF NLP
- Email filters: One of the simplest and earliest online applications of NLP is email filtering. Spam filters were the first to identify certain words or phrases that indicate a spam message. But just like early NLP adaptations, filtering has improved. In Gmail’s email classification, NLP is one of the more common and recent examples.
- Language Translation: Translation services usually ignore the fact that many languages have different sentence structures and cannot be translated directly. But they have made great progress. Because of NLP, online translators are able to translate languages more accurately and provide grammatically sound outcomes. When attempting to converse with someone in a different language, this is quite beneficial.
- Digital phone calls: We all hear “this call may be recorded for training purposes,” but do we know “How does it work?”
These recordings may be utilized for training, but most of the time, they are added to the database for an NLP system to learn from and develop in the future. Automated systems route client calls to a service agent or to online chat bots, which address client inquiries with useful data.
- Search Results: In order to help the typical person get what they need without being a search-term whiz, search engines utilize NLP to surface relevant results based on comparable search habits or user intent. As you type, Google, for instance, not only anticipates what common searches might be relevant to your inquiry, but it also looks at the big picture and understands what you’re trying to convey rather than the precise search terms.
WHY SHOULD BUSINESS USE NLP?
Natural language processing (NLP) has the ability to understand human language in context. Like machine learning, it is a subset of artificial intelligence (AI). By using NLP, you can better extract data or information from text-based documents and improve on more complex analytics tasks like sentiment analysis. They help businesses increase customer satisfaction rates and custom loyalty. NLP stands 1st choice for businesses in the future due to its role in sentiment analysis, market intelligence, customer-eccentric services, target advertising funnels and software testing.
There are a wide variety of different applications for NLP. Below are 2 different ways that companies can use the technology in their business.
- Improve user experience: A website can incorporate NLP to make it more user-friendly for visitors. Users may locate the information they’re seeking more easily by using search bar features like spell check, auto complete, and auto correct, which prevent them from leaving your website.
- Automate support: Structured, unstructured, and semi-structured data can be analyzed and extracted using robotic process automation (RPA).RPA companies must scale their NLP capabilities through AI-driven techniques in order to meet the demand and bandwidth of automation.
ROLES OF ML IN NLP
Machine learning has played a very important role in almost all of the applications of natural language processing. Let’s have a look at some of the ways that deep learning techniques have been used in natural language processing applications.
- Information Extraction: Businesses are increasingly turning away from human monitoring and control of many crucial decision. In sectors like finance, company choices are frequently influenced by public opinion and the news. Images, text, and info graphics make up the majority of the news content. Taking these texts, analyzing them, and extracting the relevant information in a way that can be used for decision-making is a significant task of natural language processing. For instance, information about a significant merger may influence company choices and be included in trading algorithms, with potentially millions of dollars in profit ramifications.
- Information Retrieval Systems: Sentiment analysis aims to examine user sentiments or views regarding a specific product. Customer relationship management now heavily relies on sentiment analysis. A single unfavorable review might have catastrophic effects on the product. Deep learning methods for sentiment analysis have become more prevalent recently. It’s interesting to note that new deep learning methods have been developed specifically for sentiment analysis, which represents the degree of research being done on sentiment analysis using deep learning.
- Question Answering Systems: A question-answering system, as its name suggests, aims to respond to queries from users. The thin line separating a dialogue system from a question-answering system has become increasingly hazy in recent years. Most frequently, a chat bot system handles the question-answering function, but the reverse is also true. So, the research work which pledges to develop a chat bot system will, in all probability, be developing a question answering system within it as well.
- Performs large-scale analysis: Scalable text analysis on a variety of documents, internal systems, emails, social media data, online reviews, and more is made possible by NLP technology. Process enormous amounts of data in a matter of seconds or minutes, whereas human analysis would take days or weeks. Additionally, NLP tools may scale up or down instantly to accommodate your demands, giving you the amount of computational power you require.
- Boosts client satisfaction: High level of customer satisfactions a business goal for every brand and a key to success . In order to ensure that a customer is never left in the dark, NLP solutions enable clients to automatically evaluate and filter customer care issues by topic, intent, urgency, sentiment, etc. and route them straight to the appropriate department or employee. By doing NLP analysis on customer satisfaction surveys, one can easily learn how satisfied consumers are at each point of their journey.
- Empowers Employees: Employees can do more complex jobs by eliminating repetitive chores. Employing NLP technology can result in a workforce that is more effective in the real world. Technology can produce a more complete data set because it can analyze large amounts of data in real-time from several sources. Employees can use the data to fulfill duties that have been allocated to them or to answer client inquiries. They are spared the time-consuming task of browsing through files. Employee engagement and satisfaction are increased when given the freedom to operate independently.
- Improved customer experience: Artificial intelligence (AI) used throughout the company can offer useful data to enhance customer interactions and question-answering. For instance, the hospitality industry relies on surveys and customer feedback to comprehend the behavior of customers. Learning how customers feel about the experience, as opposed to merely how they rate it, is a step in the process.NLP may be trained to detect sentiment in customer messages by recognizing features that hint at the underlying emotion in a phrase structure and utilizing industry-specific algorithms.
As a computerized approach of analyzing text, NLP is continually striving forward. Natural Language Processing is the practice of teaching machines to understand and interpret conversational inputs from humans. Natural language processing are designed to perform specific tasks. Some major tasks of NLP are automatic summarization, discourse analysis, machine translation, conference resolution, speech recognition, etc. Technologies like grammar checkers, conceptual search, event extraction are moving forward and leveraging NLP to advance beyond earlier technologies like string matching, keyword search, and glossary look up.