8 Real-World Examples of Natural Language Processing NLP

The review of top NLP examples shows that natural language processing has become an integral part of our lives. It defines the ways in which we type inputs on smartphones and also reviews our opinions about products, services, and brands on social media. At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions.

Each of these Natural Language Processing examples showcases its transformative capabilities. As technology evolves, we can expect these applications to become even more integral to our daily interactions, making our experiences smoother and more intuitive. Microsoft ran nearly 20 of the Bard’s plays through its Text Analytics API. The application charted emotional extremities in lines of dialogue throughout the tragedy and comedy datasets.

Natural Language Processing

The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer. Three tools used commonly for natural language processing include Natural Language Toolkit (NLTK), Gensim and Intel natural language processing Architect. NLTK is an open source Python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is another Python library for deep learning topologies and techniques. The outline of natural language processing examples must emphasize the possibility of using NLP for generating personalized recommendations for e-commerce.

Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them. 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. Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes.

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From a broader perspective, natural language processing can work wonders by extracting comprehensive insights from unstructured data in customer interactions. The global NLP market might have a total worth of $43 billion by 2025. It is a method of extracting essential features from row text so that we can use it for machine learning models. We natural language examples call it “Bag” of words because we discard the order of occurrences of words. A bag of words model converts the raw text into words, and it also counts the frequency for the words in the text. In summary, a bag of words is a collection of words that represent a sentence along with the word count where the order of occurrences is not relevant.

Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions. Start exploring the field in greater depth by taking a cost-effective, flexible specialization on Coursera. A natural language is a human language, such as English or Standard Mandarin, as opposed to a constructed language, an artificial language, a machine language, or the language of formal logic. NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful.