Natural Language Processing NLP: What it is and why it matters

This helps organisations discover what the brand image of their company really looks like through analysis the sentiment of their users’ feedback on social media platforms. By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way. An NLP customer service-oriented example would be using semantic search to improve customer experience.

DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers. Grammar and spelling is a very important factor while writing professional reports for your superiors even assignments for your lecturers. That’s why grammar and spell checkers are a very important tool for any professional writer. They can not only correct grammar and check spellings but also suggest better synonyms and improve the overall readability of your content. And guess what, they utilize natural language processing to provide the best possible piece of writing!

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In this article, we’ll delve into the fascinating world of NLP, exploring its core concepts and showcasing its power through real-world examples. NLP techniques are employed for tasks such as natural language understanding (NLU), natural language generation (NLG), machine translation, speech recognition, sentiment analysis, and more. Natural language processing systems make it easier for developers to build advanced applications such as chatbots or voice assistant systems that interact with users using NLP technology. Current approaches to NLP are based on machine learning — i.e. examining patterns in natural language data, and using these patterns to improve a computer program’s language comprehension. Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and humans through natural language. The main goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

To improve communication efficiency, companies often have to either outsource to 3rd-party service providers or use large in-house teams. AI without NLP, cannot cope with the dynamic nature of human interaction on its own. With NLP, live agents become unnecessary as the primary Point of Contact (POC).

Top 10 Applications of Natural Language Processing (NLP)

A smart-search feature offers the same autocomplete services as well as adding relevant synonyms in context to a catalogue to improve search results. Klevu is a company that provides smart search capability powered by NLP coupled with self-learning technology. Best suited for e-commerce portals, Klevu offers relevant search results and personalised search based on historical data on how a customer previously interacted with a product or service. NLP is a subset of AI that helps machines understand human intentions or the same technologies used for social media spamming can also be used for finding important information, like an email address or automatically connecting with a targeted list on LinkedIn. Marketers can benefit tremendously from natural language processing to gather more insights about their customers with each interaction.

Companies conduct many surveys to get customer’s feedback on various products. This can be very useful in understanding the flaws and help companies improve their products. Natural Language Processing is among the hottest topic in the field of data science. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow. This is a very innovative project where you want to produce titles for scientific papers.

Morphological and Lexical Analysis

As we mentioned at the beginning of this blog, most tech companies are now utilizing conversational bots, called Chatbots to interact with their customers and resolve their issues. This is a very good way of saving time for both customers and companies. The users are guided to first enter all the details that the bots ask for and only if there is a need for human intervention, the customers are connected with a customer care executive.

• Data analysis companies provide invaluable insights for growth strategies, product improvement, and market research that businesses rely on for profitability and sustainability.
• For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word «intelligen.» In English, the word «intelligen» do not have any meaning.
• This unforeseen occurrence led to the development of related models, such as Orca, which leverage the solid linguistic capabilities of Llama.
• Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words.

With NLP analysts can sift through massive amounts of free text to find relevant information. NLP can also provide answers to basic product or service questions for first-tier customer support. “NLP in customer service tools can be used as a first point of engagement to answer basic questions about products and features, such as dimensions or product availability, and even recommend similar products. This frees up human employees from routine first-tier requests, enabling them to handle escalated customer issues, which require more time and expertise. “Question Answering (QA) is a research area that combines research from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). Actually, current search engine just do ‘document retrieval’, i.e. given some keywords it only returns the relevant ranked documents that contain these keywords.

Make Sense of Unstructured data

Well, it’s simple, when you’re typing messages on a chatting application like WhatsApp. We all find those suggestions that allow us to complete our sentences effortlessly. Turns out, it isn’t that difficult to make your own Sentence Autocomplete application using NLP.

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