4 Natural Language Processing Applications and Examples for Content Marketers

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Top 30 NLP Use Cases in 2023: Comprehensive Guide

example of nlp

Every day, we say thousand of a word that other people interpret to do countless things. We, consider it as a simple communication, but we all know that words run much deeper than that. There is always some context that we derive from what we say and how we say it., NLP in Artificial Intelligence never focuses on voice modulation; it does draw on contextual patterns. One of the key advantages of Hugging Face is its ability to fine-tune pre-trained models on specific tasks, making it highly effective in handling complex language tasks. Moreover, the library has a vibrant community of contributors, which ensures that it is constantly evolving and improving.

  • Future generations will be AI-native, relating to technology in a more intimate, interdependent manner than ever before.
  • For example, swivlStudio allows you to visualize all of the utterances (what people say or ask) in one inbox.
  • The beauty of NLP is that it all happens without your needing to know how it works.
  • This is infinitely helpful when trying to communicate with someone in another language.

Marketers can also use it to tag content with important keywords and fill in other metadata that make content more visible to search engines. The Natural Language Toolkit (NLTK) is an open-source natural language processing tool made for Python. It can be customized to suit the needs of its user, whether it be a linguist or a content marketing team looking to include content analysis in their plan. It’s the process of taking words and phrases that could have multiple meanings and narrowing it down to just one. Once that’s done, a translation tool can generate a more accurate result in another language. We provide possible solutions for wide-ranging needs like speech recognition, sentiment analysis, virtual assistance and chatbots.

Smart assistants

Script-based systems capable of “fooling” people into thinking they were talking to a real person have existed since the 70s. But today’s programs, armed with machine learning and deep learning algorithms, go beyond picking the right line in reply, and help with many text and speech processing problems. Still, all of these methods coexist today, each making sense in certain use cases. One of the biggest advantages of NLP is that it can help companies make sense of large amounts of unstructured data, such as customer reviews, social media posts, and financial documents.

  • A comprehensive NLP platform from Stanford, CoreNLP covers all main NLP tasks performed by neural networks and has pretrained models in 6 human languages.
  • Document classifiers can also be used to classify documents by the topics they mention (for example, as sports, finance, politics, etc.).
  • Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume.
  • NLP is a subset of AI that helps machines understand human intentions or human language.
  • According to Statista, the NLP market is projected to grow almost 14 times larger by 2025 compared to its market size in 2017.

This disruptive AI technology allows machines to properly communicate and accurately perceive the language like humans. Businesses and companies can develop their skills and combine them with their specific products to reap the maximum benefits. Natural Language Processing or NLP represent a field of Machine Learning which provides a computer with the ability to understand and interpret the human language and process it in the same manner. Machine Translation has profoundly impacted global communication, breaking down language barriers and enabling seamless cross-cultural interactions in various domains, including business, education, and diplomacy. Conversational banking can also help credit scoring where conversational AI tools analyze answers of customers to specific questions regarding their risk attitudes. NLP is used to build medical models which can recognize disease criteria based on standard clinical terminology and medical word usage.

Applications of NLP

For example, AI-driven chatbots are being used by banks, airlines, and other businesses to provide customer service and support that is tailored to the individual. Natural Language Processing (NLP) is a rapidly growing field that is revolutionizing the way we interact with technology. In this post, we’ll explore 10 examples of NLP applications across different industries to drive business success. Smart assistants are exemplary Natural Language Processing (NLP) applications that utilize advanced algorithms to comprehend and reply to user voice commands and questions. Natural Language Processing (NLP) offers numerous advantages that have revolutionized human-technology interactions and text management. Firstly, NLP enhances the user experience by enabling more natural communication through voice-activated assistants and chatbots.

If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. In this piece, we’ll go into more depth on what NLP is, take you through a number of natural language processing examples, and show you how you can apply these within your business. Another kind of model is used to recognize and classify entities in documents.

NLP Projects Idea #2 Market Basket Analysis

Natural language processing may have started as a purely academic tool, but real-world applications in content marketing continue to grow. NLP, AI, and machine learning allow brands to pinpoint the exact audience for their product or service and target them with the right content. It makes research, planning, creating, tracking, and scaling content an achievable goal instead of a marketing pipe dream. Content marketers also use sentiment analysis to track reactions to their own content on social media. Sentiment analysis tools look for trigger words like wonderful or terrible. They also try to analyze the semantic meaning behind posts by putting them into context.

example of nlp

Since then, filters have been continuously upgraded to cover more use cases. Email filters are common NLP examples you can find online across most servers. Thanks to NLP, you can analyse your survey responses accurately and effectively without needing to invest human resources in this process. A spam filter is probably the most well known and established application of email filters.

Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing has the ability to interrogate the data with natural language text or voice. This is also called “language in.” Most consumers have probably interacted with NLP without realizing it.

example of nlp

For example, the words “helping” and “helper” share the root “help.” Stemming allows you to zero in on the basic meaning of a word rather than all the details of how it’s being used. NLTK has more than one stemmer, but you’ll be using the Porter stemmer. When you use a list comprehension, you don’t create an empty list and then add items to the end of it. An NLP system can be trained to summarize the text more readably than the original text. This is useful for articles and other lengthy texts where users may not want to spend time reading the entire article or document. Word processors like MS Word and Grammarly use NLP to check text for grammatical errors.

First, the concept of Self-refinement explores the idea of LLMs improving themselves by learning from their own outputs without human supervision, additional training data, or reinforcement learning. A complementary area of research is the study of Reflexion, where LLMs give themselves feedback about their own thinking, and reason about their internal states, which helps them deliver more accurate answers. Most NLP systems are developed and trained on English data, which limits their effectiveness in other languages and cultures. Developing NLP systems that can handle the diversity of human languages and cultural nuances remains a challenge due to data scarcity for under-represented classes.

example of nlp

It converts a large set of text into more formal representations such as first-order logic structures that are easier for the computer programs to manipulate notations of the natural language processing. The effective implementation of NLP made the language translation process easier. This is beneficial when trying to communicate with someone in another language.

Benefits of NLP

Natural language processing is developing at a rapid pace and its applications are evolving every day. 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. NLP is used for automatically translating text from one language into another using deep learning methods like recurrent neural networks or convolutional neural networks.

example of nlp

If you are new to NLP, then these NLP full projects for beginners will give you a fair idea of how real-life NLP projects are designed and implemented. A major drawback of statistical methods is that they require elaborate feature engineering. Since 2015,[21] the statistical approach was replaced by neural networks approach, using word embeddings to capture semantic properties of words. US retailer Nordstrom analyzed the amount of customer feedback collected through comments, surveys and thank you’s. A company’s customer service costs a lot of time and money, especially when they’re growing.


Auto-GPT, a viral open-source project, has become one of the most popular repositories on Github. For instance, you could request Auto-GPT’s assistance in conducting market research for your next cell-phone purchase. It could examine top brands, evaluate various models, create a pros-and-cons matrix, help you find the best deals, and even provide purchasing links. The development of autonomous AI agents that perform tasks on our behalf holds the promise of being a transformative innovation. Second, the integration of plug-ins and agents expands the potential of existing LLMs.

example of nlp

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