14 Natural Language Processing Examples NLP Examples
NLP can also scan patient documents to identify patients who would be best suited for certain clinical trials. NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language.
That’s great news for businesses since NLP can have a dramatic effect on how you run your day-to-day operations. It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers. Through NLP, computers don’t just understand meaning, they also understand sentiment and intent.
Computer Vision Applications in 10 Industries
The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning. The entity recognition task involves detecting mentions of specific types of information in natural language input. Typical entities of interest for entity recognition natural language programming examples include people, organizations, locations, events, and products. Sentence breaking is done manually by humans, and then the sentence pieces are put back together again to form one
coherent text. Sentences are broken on punctuation marks, commas in lists, conjunctions like “and”
or “or” etc.
- That’s why machine learning and artificial intelligence (AI) are gaining attention and momentum, with greater human dependency on computing systems to communicate and perform tasks.
- The NLTK Python framework is generally used as an education and research tool.
- To some extent, it is also possible to auto-generate long-form copy like blog posts and books
with the help of NLP algorithms.
- As shown above, the final graph has many useful words that help us understand what our sample data is about, showing how essential it is to perform data cleaning on NLP.
Notice that the most used words are punctuation marks and stopwords. In the example above, we can see the entire text of our data is represented as sentences and also notice that the total number of sentences here is 9. By tokenizing the text with sent_tokenize( ), we can get the text as sentences. For various data processing cases in NLP, we need to import some libraries. In this case, we are going to use NLTK for Natural Language Processing.
Natural Language Processing (NLP) with Python — Tutorial
Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.
