The world of business would be greatly benefited from in-depth insights that are controlled by AI. It will help in increasing customer satisfaction rates, improve the revenue curve & ultimately transform the future of business operations. Today, many innovative companies are perfecting their NLP algorithms by using a managed workforce for data annotation, an area where CloudFactory shines.

This is a common NLP practice followed by every business that consists of digital telecommunications and customer service. An example of this can be seen in Vyrb app, that Appinventiv developed for its client, Innovative Eyewear. Vyrb is a voice assistant app for social media that allows voice posting on platforms such as Twitter and Facebook using bluetooth glasses and other wearables. This is a classic example of how organizations can utilize NLP-based smart assistants for their modern business processes. Business decisions are difficult to make, and the best decisions are a product of data-driven insights.

Pharma Literature Mining for Drug Development

When everything comes together, new challenges arise, such as grammatical conventions and word dependency on each other. Machine Learning) to meet the objective of Artificial Intelligence. The ultimate goal is to bridge how people communicate and what computers can understand. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years.

Categorization is also known as text classification and text tagging. Standardizing individual words by reducing them to their root forms. Tagging specific parts of speech—such as nouns, verbs, and adjectives. If you’ve ever tried to learn a foreign language, you’ll know that natural language processing with python solutions language can be complex, diverse, and ambiguous, and sometimes even nonsensical. English, for instance, is filled with a bewildering sea of syntactic and semantic rules, plus countless irregularities and contradictions, making it a notoriously difficult language to learn.

Topic Modeling – can be used to understand what the text and its elements are about. Allied Market Research, the cognitive computing market will be worth $13.7 billion by 2020, growing at a 33.1 percent compound annual growth rate over current levels. Clinical trial matching is perhaps the most talked-about use case in the “developing” category.

NLP use cases

If you are looking for a service partner, we suggest you work with Shaip and take your patient care solutions a notch higher. More advanced NLP models can even identify specific features and functions of products in online content to understand what customers like and dislike about them. Marketers then use those insights to make informed decisions and drive more successful campaigns. The healthcare industry also uses NLP to support patients via teletriage services. In practices equipped with teletriage, patients enter symptoms into an app and get guidance on whether they should seek help. NLP applications have also shown promise for detecting errors and improving accuracy in the transcription of dictated patient visit notes.

Watson Natural Language Processing

Our solution automated this task by creating a metadata repository of the documents. NLP can help healthcare professionals with documentation needs to minimize their time on documentation and focus more on their crucial responsibilities. For example, NLP can summarize documents, find the key information, or convert an image into text to easily record electronically.

A dedicated Customer Service process can help companies improve their products and increase customer satisfaction. However, manually interacting with each customer can be a tedious task, and that’s where chatbots come into play to help companies achieve the goal of a seamless customer experience. Many companies use chatbots for their apps and websites to solve a customer’s fundamental problems. Chatbots not only simplify the process for companies but also save customers from the frustration of waiting to interact with customer service. It relies on the data that it catalogs based on what the other millions of Google users are searching for when inputting search terms.

NLP use cases

In the current digital landscape, NLP based applications and software are being leveraged in every industry for every aspect of emergency management. Customer service and experience is the most crucial part of any business. Writing tools, including Grammarly, WhiteSmoke, and ProWritingAid, rely on the use of NLP to correct grammatical and spelling errors.


Summarization by abstractive methods is a way of summary creation by the generation of new sentences and phrases as compared to the source document. This type of method is often more difficult to execute and needs more advanced approaches like Deep Learning. Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Keep your volume distribution within a specified range when training your initial model to establish a baseline performance reading. As a starting point, aim for an average of 15 examples per intent, but allow no fewer than seven and no more than 25 per intent. A significant difference in the number of training examples per intent can cause serious issues.

NLP use cases

Employees might not appreciate you taking them away from their regular work, which can lead to reduced productivity and increased employee churn. While larger enterprises might be able to get away with creating in-house data-labeling teams, they’re notoriously difficult to manage and expensive to scale. Automatic labeling, or auto-labeling, is a feature in data annotation tools for enriching, annotating, and labeling datasets.

What are the benefits of natural language processing?

The right messaging channels create a seamless, quality feedback loop between your team and the NLP team lead. You get increased visibility and transparency, and everyone involved can stay up-to-date on progress, activities, and future use cases. Even before you sign a contract, ask the workforce you’re considering to set forth a solid, agile process for your work. If your chosen NLP workforce operates in multiple locations, providing mirror workforces when necessary, you get geographical diversification and business continuity with one partner. Syntax analysis is analyzing strings of symbols in text, conforming to the rules of formal grammar. Categorization is placing text into organized groups and labeling based on features of interest.

Similarly, another experiment was carried out in order to automate the identification as well as risk prediction for heart failure patients that were already hospitalized. Natural Language Processing was implemented in order to analyze free text reports from the last 24 hours, and predict the patient’s risk of hospital readmission and mortality over the time period of 30 days. At the end of the successful experiment, the algorithm performed better than expected and the model’s overall positive predictive value stood at 97.45%.

Get an overview of how Natural Language Processing can be used in the healthcare sector. The benefits of deploying NLP can definitely be applied to other areas of interest and a myriad of algorithms can be deployed in order to pick out and predict specified conditions amongst patients. Another “virtual therapist” started by Woebot connects patients through Facebook messenger. According to a trial, it has gained success in lowering anxiety and depression in 82% of the college students who joined in. This blog is almost about2200+ wordslong and may take~9 minsto go through the whole thing. Alldus are privileged to be working with some of the companies leading the way in AI in Healthcare.

Natural Language Processing (NLP) Use Cases for Business Optimization

Another major benefit of NLP is that you can use it to serve your customers in real-time through chatbots and sophisticated auto-attendants, such as those in contact centers. The answer to each of those questions is a tentative YES—assuming you have quality data to train your model throughout the development process. Our client, a global pharmaceutical leader, required peering over 7 Mn documents to capture the many parameters specific to the trials it was conducting.

Preparing an NLP dataset

Yet, it’s not a complete toolkit and should be used along with NLTK or spaCy. Rules are also commonly used in text preprocessing needed for ML-based NLP. For example, tokenization and part-of-speech tagging (labeling nouns, verbs, etc.) are successfully performed by rules. They’re written manually and provide some basic automatization to routine tasks. Intelligent Document Processing is a technology that automatically extracts data from diverse documents and transforms it into the needed format. It employs NLP and computer vision to detect valuable information from the document, classify it, and extract it into a standard output format.

Wide-ranging applications for AI-powered insights

Computational phenotyping enables patient diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction , etc. 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 tagging, optical character recognition , handwriting recognition, etc. For their part, natural language processing solutions can help bridge the gap between complex medical terms and patients’ understanding of their health. Many clinicians utilize NLP as an alternative method of typing and handwriting notes. As NLP doesn’t come as a standard one-size-fits-all solution, it is important to harness the experience of leading technology platforms to build a customized healthcare option for your particular need.

Data labeling for NLP explained

Going forward it is likely this trend will continue thus having a great impact on everything from drug discovery to patient rehabilitation and revolutionizing how healthcare is practiced. NLP is giving researchers a much-needed head-start in the drug discovery process, allowing them to quickly learn about similar diseases by extracting information from unstructured sources. In late 2019, AI-platform BlueDot identified a cluster of pneumonia-like cases in Wuhan, noticing similarities with the SARS virus. BlueDot uses NLP to cull data from thousands of disparate sources before alerting physicians to anomalies. While access to such vast amounts of data may seem like a good thing, it is of little use unless it can be properly analyzed to gain insights.

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