14 Natural Language Processing Examples NLP Examples

Augmented Transition Networks is a finite state machine that is capable of recognizing regular languages. Several websites contain a feature of implementing chatbots so that business-related queries and valuable information can be exchanged effectively. The processed data will be fed to a classification algorithm (e.g. decision tree, KNN, random forest) in order to classify the data into spam or ham (i.e. non-spam email). Credit scoring is a statistical analysis performed by lenders, banks, and financial institutions to determine the creditworthiness of an individual or a business. As models continue to become more autonomous and extensible, they open the door to unprecedented productivity, creativity, and economic growth.

Computational phenotyping enables patient diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), etc. By supplying information on market sentiment and enabling investors to modify their strategies as necessary, sentiment research can assist investors in making more educated investment decisions. For instance, if a stock is receiving a lot of positive sentiment, an investor may consider buying more shares, while negative sentiment may prompt them to sell or hold off on buying. Despite these uncertainties, it is evident that we are entering a symbiotic era between humans and machines.

What Is Natural Language Understanding (NLU)?

According to a report by the US Bureau of Labor Statistics, the jobs for computer and information research scientists are expected to grow 22 percent from 2020 to 2030. As per the Future of Jobs Report released by the World Economic Forum in October 2020, humans and machines will be spending an equal amount of time on current tasks in the companies, by 2025. The report has also revealed that about 40% of the employees will be required to reskill and 94% of the business leaders expect the workers to invest in learning new skills. One such sub-domain of AI that is gradually making its mark in the tech world is Natural Language Processing (NLP).

Therefore, I have put together a list of the top 10 applications of natural language processing. In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important. NLP algorithms focus on linguistics, computer science, and data analysis to provide machine translation capabilities for real-world applications.

Intelligent Document Processing: Technology Overview

You can easily appreciate this fact if you start recalling that the number of websites or mobile apps, you’re visiting every day, are using NLP-based bots to offer customer support. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines. One of the first natural language processing examples for businesses Twiggle is known for offering advanced creations in AI, ML, and NLP on the market.

Monitoring and evaluation of what customers are saying about a brand on social media can help businesses decide whether to make changes in brand or continue as it is. Social media listening tool such as Sprout Social help monitor, evaluate and analyse social media activity concerning a particular brand. The services sports a user-friendly interface does not require a ton of input for it to run. Natural Language Processing (NLP), Cognitive services and AI an increasingly popular topic in business and, at this point, seems all but necessary for successful companies. NLP holds power to automate support, analyse feedback and enhance customer experiences.

Another Python library, Gensim was created for unsupervised information extraction tasks such as topic modeling, document indexing, and similarity retrieval. But it’s mostly used for working with word vectors via integration with Word2Vec. The tool is famous for its performance and memory optimization capabilities allowing it to operate huge text files painlessly. Yet, it’s not a complete toolkit and should be used along with NLTK or spaCy.

For instance, NLP is the core technology behind virtual assistants, such as the Oracle Digital Assistant (ODA), Siri, Cortana, or Alexa. When we ask questions of these virtual assistants, NLP is what enables them to not only understand the user’s request, but to also respond in natural language. NLP applies both to written text and speech, and can be applied to all human languages. Other examples of tools powered by NLP include web search, email spam filtering, automatic translation of text or speech, document summarization, sentiment analysis, and grammar/spell checking. For example, some email programs can automatically suggest an appropriate reply to a message based on its content—these programs use NLP to read, analyze, and respond to your message.

Translation

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