Natural Language Processing NLP Examples

Language models are AI models which rely on NLP and deep learning to generate human-like text and speech as an output. Language models are used for machine translation, part-of-speech (PoS) tagging, optical character recognition (OCR), handwriting recognition, etc. Chatbots, sentiment analysis, speech recognition, text summarization and machine translation are some examples of real-world applications of NLP. These natural language processing examples are only the tip of the iceberg when it comes to the possibilities of what can be done with NLP software. Personalized marketing is one possible use for natural language processing examples.

Machine learning AIs have advanced to the level today where natural language processing can analyze, extract meaning from, and determine actionable insights from both syntax and semantics in text. NLU tools should be able to tag and categorize the text they encounter appropriately. In order to categorize or tag texts with humanistic dimensions such as emotion, effort, intent, motive, intensity, and more, Natural Language Understanding systems leverage both rules based and statistical machine learning approaches. Of course, Natural Language Understanding can only function well if the algorithms and machine learning that form its backbone have been adequately trained, with a significant database of information provided for it to refer to.

Real-Life Examples of NLP

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. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data. Natural Language Processing plays a vital role in our digitally connected world.

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. And similarly, many other sites used the NLP solutions to detect duplications of questions or related searches. And this is how natural language processing techniques and algorithms work.

NLP Chatbot and Voice Technology Examples

It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one. These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP. Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries. Spellcheck is one of many, and it is so common today that it’s often taken for granted.

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