4 Natural Language Processing Applications and Examples for Content Marketers

In 1950, the British computer scientist Alan Turing proposed a test to determine a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. In addition, there are cloud-based LLMs such as OpenAI’s GPT-3 and Meta’s LLaMA, which are disrupting the field. Beyond just its awesome data analyzation capabilities, NLP has a number of benefits that a company in any industry would appreciate. Ideally, your NLU solution should be able to create a highly developed interdependent network of data and responses, allowing insights to automatically trigger actions. Your NLU solution should be simple to use for all your staff no matter their technological ability, and should be able to integrate with other software you might be using for project management and execution.

• Then in 2017, Vaswani et al introduced the transformer architecture in a paper called “Attention is All You Need”, which was yet another quantum leap in the field.
• Traditional Business Intelligence (BI) tools such as Power BI and Tableau allow analysts to get insights out of structured databases, allowing them to see at a glance which team made the most sales in a given quarter, for example.
• And when it comes to quality training data, Cogito is a leading marketplace for it.
• Delivering the best customer experience and staying compliant with financial industry regulations can be driven through conversation analytics.
• If the review is mostly positive, the companies get an idea that they are on the right track.
• Retailers claim that on average, e-commerce sites with a semantic search bar experience a mere 2% cart abandonment rate, compared to the 40% rate on sites with non-semantic search.

However, with the advent of neural networks and deep learning techniques in NLP, these pipelines are becoming less relevant. Natural language processing, or NLP for short, is a revolutionary new solution that is helping companies enhance their insights and get even more visibility into all facets of their customer-facing operations than ever before. In fact, a 2019 Statista report projects that the NLP market will increase to over $43 billion dollars by 2025. Here is a breakdown of what exactly natural language processing is, how it’s leveraged, and real use case scenarios from some major industries. In our research, we’ve found that more than 60% of consumers think that businesses need to care more about them, and would buy more if they felt the company cared. Part of this care is not only being able to adequately meet expectations for customer experience, but to provide a personalized experience.

NLP Limitations

This helps to identify pain points in customer experience, inform decisions on where to focus improvement efforts, and track changes in customer sentiment over time. In our journey through some Natural Language Processing examples, we’ve seen how NLP transforms our interactions—from search engine queries and machine translations to voice assistants and sentiment analysis. These examples illuminate the profound impact of such a technology on our digital experiences, underscoring its importance in the evolving tech landscape. At its most basic, natural language processing is the means by which a machine understands and translates human language through text. NLP technology is only as effective as the complexity of its AI programming. Government agencies are bombarded with text-based data, including digital and paper documents.

Natural language processing is described as the interaction between human languages and computer technology. Often overlooked or may be used too frequently, NLP has been missed or skipped on many occasions. If you are looking for NLP in healthcare projects, then this project is a must try. Natural Language Processing (NLP) can be used for diagnosing diseases by analyzing the symptoms and medical history of patients expressed in natural language text.

People go to social media to communicate, be it to read and listen or to speak and be heard. As a company or brand you can learn a lot about how your customer feels by what they comment, post about or listen to. Applications of natural language processing seek to make our lives easier and more efficient. Machine learning can detect linguistic characteristics that relate to the way people feel. For example, Python can pick up on positive, negative, and neutral words as well as certain patterns of written language. As a result, we can get an overview of the way people are interacting with an article, video, blog post, or image.

Natural language processing projects

The poor grammar indicates that you didn’t do your foreign language studies. In the past, translation services often ignored that many languages don’t lend themselves to literal translation and have distinct sentence structure ordering. “According to the FBI, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. Insurance fraud affects both insurers and customers, who end up paying higher premiums to cover the cost of fraudulent claims. Insurers can use NLP to try to mitigate the high cost of fraud, lower their claims payouts and decrease premiums for their customers.

They employ a mechanism called self-attention, which allows them to process and understand the relationships between words in a sentence—regardless of their positions. This self-attention mechanism, combined with the parallel processing capabilities of transformers, helps them achieve more efficient and accurate language modeling than their predecessors. Today, NLP has begun to be widely used in consumer electronics as well as in business. Insurance, pharma or legal firms which need to process large numbers of documents may well resort to NLP to extract structured information, cluster items, analyse customer support logs, or predict future events. In 2013, a team at Google introduced the Word2vec algorithm, which represents words in a lexicon as points in vector space, where the distance between points is significant and corresponds to semantic similarity.

The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output. As artificial intelligence has advanced, so too has natural language processing (NLP) technology. NLP is the branch of AI that focuses on enabling computers to understand human language in all its complexity.

Duplicate detection makes sure that you see a variety of search results by collating content re-published on multiple sites. Any time you type while composing a message or a search query, NLP will help you type faster. Natural language processing with Python is helpful when it comes to predicting text.

Products and services

Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. This way, you can set up custom tags for your inbox and every incoming email that meets the set requirements will be sent through the correct route depending on its content. Thanks to NLP, you can analyse your survey responses accurately and effectively without needing to invest human resources in this process.

It’s been said that language is easier to learn and comes more naturally in adolescence because it’s a repeatable, trained behavior—much like walking. 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. And as AI and augmented analytics get more sophisticated, so will Natural Language Processing (NLP). While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives. Natural language processing can be an extremely helpful tool to make businesses more efficient which will help them serve their customers better and generate more revenue. As these examples of natural language processing showed, if you’re looking for a platform to bring NLP advantages to your business, you need a solution that can understand video content analysis, semantics, and sentiment mining.

A good application of this NLP project in the real world is using this NLP project to label customer reviews. The companies can then use the topics of the customer reviews to understand where the improvements should be done on priority. Companies conduct many surveys to get customer’s feedback on various products.

This tool learns about customer intentions with every interaction, then offers related results. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. As digital transformation continues to rewrite the rules of conducting business, communication technology, particularly… Considering these metrics in mind, it helps to evaluate the performance of an NLP model for a particular task or a variety of tasks.

Natural Language Processing (NLP): 7 Key Techniques

And with the emergence of Chat GPT and the sudden popularity of large language models, expectations are even higher. Users want AI to handle more complex questions, requests, and conversations. Another area where NLP is making significant headway is in the realm of digital marketing. By analyzing customer sentiment and behavior, NLP-powered marketing tools can generate insights that help marketers create more effective campaigns and personalized content. This technology can also be used to optimize search engine rankings by improving website copy and identifying high-performing keywords.

It helps NLP systems understand the syntactic structure and meaning of sentences. In our example, dependency parsing would identify “I” as the subject and “walking” as the main verb. Part-of-speech (POS) tagging identifies the grammatical category of each word in a text, such as noun, verb, adjective, or adverb.

The next natural language processing classification text analytics converts unstructured text data into structured and meaningful data for further analysis. The data converted for the analysis procedure is taken by using different linguistics, statistical, and machine learning techniques. 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).

With NLP, computers can decipher meaning from text or speech, recognize patterns in language, and even generate their own human-like responses. While computers communicate with one another in code and long lines of ones and zeros, they’ve come to better understand human language with natural language processing (NLP) and machine learning (ML). With these natural language processing and machine learning methods, technology can more easily grasp human intent, even with colloquialisms, slang, or a lack of greater context.

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