6 Real-World Examples of Natural Language Processing

Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise. For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

The keyword extraction task aims to identify all the keywords from a given natural language input. Utilizing keyword

extractors aids in different uses, such as indexing data to be searched or creating tag clouds, among other things. The text classification task involves assigning a category or class to an arbitrary piece of natural language input such

as documents, email messages, or tweets. Text classification has many applications, from spam filtering (e.g., spam, not

spam) to the analysis of electronic health records (classifying different medical conditions). The next step in natural language processing is to split the given text into discrete tokens. These are words or other

symbols that have been separated by spaces and punctuation and form a sentence.

Lack of Trust Towards Machines

It can be used to analyze social media posts,

blogs, or other texts for the sentiment. Companies like Twitter, Apple, and Google have been using natural language

processing techniques to derive meaning from social media activity. To explain in detail, the semantic search engine processes the entered search query, understands not just the direct

sense but possible interpretations, creates associations, and only then searches for relevant entries in the database. Since the program always tries to find a content-wise synonym to complete the task, the results are much more accurate

and meaningful.

Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated. This is done by using NLP to understand what the customer needs based on the language they are using.

Example 1: Syntax and Semantics Analysis

Text analytics converts unstructured text data into meaningful data for analysis using different linguistic, statistical, and machine learning techniques. Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience. Additional ways that NLP helps with text analytics are keyword extraction and finding structure or patterns examples of natural language processing in unstructured text data. There are vast applications of NLP in the digital world and this list will grow as businesses and industries embrace and see its value. While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation. It’s an intuitive behavior used to convey information and meaning with semantic cues such as words, signs, or images.

Sentiment analysis is widely applied to reviews, surveys, documents and much more. Let’s look at some of the most popular techniques used in natural language processing. Note how some of them are closely intertwined and only serve as subtasks for solving larger problems. Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words.

In this exploration, we’ll journey deep into some Natural Language Processing examples, as well as uncover the mechanics of how machines interpret and generate human language. Indeed, programmers used punch cards to communicate with the first computers 70 years ago. This manual and arduous process was understood by a relatively small number of people.

• Pragmatic level focuses on the knowledge or content that comes from the outside the content of the document.
• Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar.
• Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information.
• Entity recognition helps machines identify names, places, dates, and more in a text.
• In second model, a document is generated by choosing a set of word occurrences and arranging them in any order.
• The tokens or ids of probable successive words will be stored in predictions.