10 Examples of Natural Language Processing in Action

CodaMetrix (CMX) is a SaaS technology company providing an AI-powered platform to facilitate healthcare revenue cycle management and medical coding. The company’s platform combines machine learning (ML), deep learning, and natural language… Eigen’s natural language processing to work on your documents to answer your unique questions. Fast-moving organizations in highly-scrutinized industries use Eigen to get down to the data points that drive their businesses. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.

The recent proliferation of sensors and Internet-connected devices has led to an explosion in the volume and variety of data generated. As a result, many organizations leverage NLP to make sense of their data to drive better business decisions. Mail us on h[email protected], to get more information about given services. Syntactic Ambiguity exists in the presence of two or more possible meanings within the sentence.

Modeling

Let us start with a simple example to understand how to implement NER with nltk . It is a very useful method especially in the field of claasification problems and search egine optimizations. It is clear that the tokens of this category are not significant.

Now that you have relatively better text for analysis, let us look at a few other text preprocessing methods. For example, suppose an employee tries to copy confidential information somewhere outside the company. In that case, these systems will not allow the device to make a copy and will alert the administrator to stop this security breach. For example, the Loreal Group used an AI chatbot called Mya to increase the efficiency of its recruitment process. As you start typing, Google will start translating every word you say into the selected language. Above, you can see how it translated our English sentence into Persian.

Speech Recognition

Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. These smart assistants, such as Siri or Alexa, use voice recognition to understand our everyday queries, they then use natural language generation (a subfield of NLP) to answer these queries. Search engines no longer just use keywords to help users reach their search results. They now analyze people’s intent when they search for information through NLP.

This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. Our paper is the first to explore using CDA techniques to create singular-they examples for NLP training data. By replacing pronouns like he and she in the original training data with they, we created 63,000 additional sentences with singular they.

They are beneficial for eCommerce store owners in that they allow customers to receive fast, on-demand responses to their inquiries. This is important, particularly for smaller companies that don’t have the resources to dedicate a full-time customer support agent. By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way. 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. If a particular word appears multiple times in a document, then it might have higher importance than the other words that appear fewer times (TF).

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