6 Real-World Examples of Natural Language Processing

Query and Document Understanding build the core of Google search. In layman’s terms, a Query is your search term and a Document is a web page. Because we write them using our language, NLP is essential nlp example in making search work. The beauty of NLP is that it all happens without your needing to know how it works. Many people don’t know much about this fascinating technology, and yet we all use it daily.

If you’re currently collecting a lot of qualitative feedback, we’d love to help you glean actionable insights by applying NLP. Auto-correct finds the right search keywords if you misspelled something, or used a less common name. When you search on Google, many different NLP algorithms help you find things faster.

Activation Functions

We hope you enjoyed reading this article and learned something new. Any suggestions or feedback is crucial to continue to improve. For this tutorial, we are going to focus more on the NLTK library. Let’s dig deeper into natural language processing by making some examples. For instance, the freezing temperature can lead to death, or hot coffee can burn people’s skin, along with other common sense reasoning tasks. However, this process can take much time, and it requires manual effort.

Lemmatization, on the other hand, is a systematic step-by-step process for removing inflection forms of a word. It makes use of vocabulary, word structure, part of speech tags, and grammar relations. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system.

LSA (Latent semantic analysis)

With greater potential in itself already, Artificial intelligence’s subset Natural language processing can derive meaning from human languages. Abstractive summarization is the new state of art method, which generates new sentences that could best represent the whole text. This is better than extractive methods where sentences are just selected from original text for the summary. https://www.metadialog.com/ SaaS tools, on the other hand, are ready-to-use solutions that allow you to incorporate NLP into tools you already use simply and with very little setup. Connecting SaaS tools to your favorite apps through their APIs is easy and only requires a few lines of code. It’s an excellent alternative if you don’t want to invest time and resources learning about machine learning or NLP.

It’s time to initialize the summarizer model and pass your document and desired no of sentences as input. It is based on the concept that words which occur more frequently are significant. Hence , the sentences containing highly frequent words are important . The proposed test includes a task that involves the automated interpretation and generation of natural language.

A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmatically, you first need to preprocess it. In this tutorial, you’ll take your first look at the kinds of text preprocessing tasks you can do with NLTK so that you’ll be ready to apply them in future projects. You’ll also see how to do some basic text analysis and create visualizations. It is the branch of Artificial Intelligence that gives the ability to machine understand and process human languages.

• This tool learns about customer intentions with every interaction, then offers related results.
• You can decide the number of sentences you want in the summary through parameter sentences_count.
• This concept uses AI-based technology to eliminate or reduce routine manual tasks in customer support, saving agents valuable time, and making processes more efficient.
• Machine learning and natural language processing technology also enable IBM’s Watson Language Translator to convert spoken sentences into text, making communication that much easier.
• Natural language processing (NLP) is behind the accomplishment of some of the things that you might be disregard on a daily basis.

Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information. This is a NLP practice that many companies, including large telecommunications providers have put to use. NLP also enables computer-generated language close to the voice of a human.

Virtual assistants

When you use a concordance, you can see each time a word is used, along with its immediate context. This can give you a peek into how a word is being used at the sentence level and what words are used with it. While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases. Some sources also include the category articles (like “a” or “the”) in the list of parts of speech, but other sources consider them to be adjectives. Part of speech is a grammatical term that deals with the roles words play when you use them together in sentences. Tagging parts of speech, or POS tagging, is the task of labeling the words in your text according to their part of speech.

By using NLP technology, a business can improve its content marketing strategy. Quora like applications use duplicate detection technology to keep the site functioning smoothly. This is how an NLP offers services to the users and ultimately gives an edge to the organization by aiding users with different solutions.

For instance, we have a database of thousands of dog descriptions, and the user wants to search for “a cute dog” from our database. The job of our search engine would be to display the closest response nlp example to the user query. The search engine will possibly use TF-IDF to calculate the score for all of our descriptions, and the result with the higher score will be displayed as a response to the user.