11 Real-Life Examples of NLP in Action

Chatbots without NLP rely majorly on pre-fed static information & are naturally less equipped to handle human languages that have variations in emotions, intent, and sentiments to express each specific query. The best approach towards NLP that is a blend of Machine Learning and Fundamental Meaning for maximizing the outcomes. Machine Learning only is at the core of many NLP platforms, however, the amalgamation of fundamental meaning and Machine Learning helps to make efficient NLP based chatbots. Machine Language is used to train the bots which leads it to continuous learning for natural language processing (NLP) and natural language generation (NLG). Best features of both the approaches are ideal for resolving the real-world business problems. NLP is used to understand the structure and meaning of human language by analyzing different aspects like syntax, semantics, pragmatics, and morphology.

TextBlob is a Python library that works as an extension of NLTK, allowing you to perform the same NLP tasks in a much more intuitive and user-friendly interface. Its learning curve is more simple than with other open-source libraries, so it’s an excellent choice for beginners, who want to tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more. For example, any company that collects customer feedback in free-form as complaints, social media posts or survey results like NPS, can use NLP to find actionable insights in this data. With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content.

How to create a Python library

Natural language understanding (NLU) allows machines to understand language, and natural language generation (NLG) gives machines the ability to “speak.”Ideally, this provides the desired response. The practice of automatic insights for better delivery of services is one of the next big natural language processing examples. For making the solution easy, Quora uses NLP for reducing the instances of duplications.

• Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information.
• By using it, companies can take advantage of their automation processes for delivering solutions to customers faster.
• Apart from allowing businesses to improve their processes and serve their customers better, NLP can also help people, communities, and businesses strengthen their cybersecurity efforts.

In layman’s terms, a Query is your search term and a Document is a web page. Because we write them using our language, NLP is essential in making search work. The beauty of NLP is that it all happens without your needing to know how it works. Any time you type while composing a message or a search query, NLP helps you type faster.

Python and the Natural Language Toolkit (NLTK)

This feature allows a user to speak directly into the search engine, and it will convert the sound into text, before conducting a search. In the 1950s, Georgetown and IBM presented the first NLP-based translation machine, which had the ability to translate 60 Russian sentences to English automatically. NLP engines extract valuable information from a sentence, whether typed or spoken and translate it into structured data. Request your free demo today to see how you can streamline your business with natural language processing and MonkeyLearn. People go to social media to communicate, be it to read and listen or to speak and be heard.

Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets. When a user punches in a query for the chatbot, the algorithm kicks in to break that query down into a structured string of data that is interpretable by a computer. The process of derivation of keywords and useful data from the user’s speech input is termed Natural Language Understanding (NLU). NLU is a subset of NLP and is the first stage of the working of a chatbot. In fact, a report by Social Media Today states that the quantum of people using voice search to search for products is 50%. With that in mind, a good chatbot needs to have a robust NLP architecture that enables it to process user requests and answer with relevant information.

Deep 6 AI

Companies can then apply this technology to Skype, Cortana and other Microsoft applications. Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services. There are many eCommerce websites and online retailers that leverage NLP-powered semantic search engines. They aim to understand the shopper’s intent when searching for long-tail keywords (e.g. women’s straight leg denim size 4) and improve product visibility. An NLP customer service-oriented example would be using semantic search to improve customer experience.

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools.

Also, without marketing, circulating the ideology of business with the globe is a bit challenging. Also, NLP enables the computer to generate language which is close to the voice of a human. For example- Phone calls for scheduling appointments like haircuts, restaurant timings, etc, can be scheduled with the help of NLP.

Though we can expect the number of natural languages, prebuilt models, and integrations to grow over time. NLP engines use human language corpus to extract the meaning of user requests and understand common phrases. Using Lex, organizations can tap on various deep learning functionalities. The technology can be used for creating more engaging User experience using applications. Predictive analysis and autocomplete works like search engines predicting things based on the user search typing and then finishing the search with suggested words.

Entities can be names, places, organizations, email addresses, and more. They can help you easily classify support tickets by topic, to speed up your processes and deliver powerful insights. The use of NLP in the insurance industry allows companies to leverage text analytics and NLP for informed decision-making for critical claims and risk management processes. Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices.

Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. Levity offers its own version of email classification through using NLP. This way, you can set up custom tags for your inbox and every incoming email that meets the set requirements will be sent through the correct route depending on its content.

Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis. Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction. TextBlob is a Python module that extends NLTK, allowing you to execute the same NLP operations in a much more intuitive and user-friendly interface. It has a simpler learning curve than other open-source libraries, making it a good alternative for beginners who wish to tackle NLP tasks such as sentiment analysis, text categorization, part-of-speech tagging, and much more.

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