Top 7 Applications of NLP Natural Language Processing

The Hitachi Solutions team are experts in helping organizations put their data to work for them. Our accessible and effective natural language processing solutions can be tailored to any industry and any goal. In the field of education, NLP is used by many edtech companies and institutions to develop intelligent tutoring systems, automatic assessment of responses, and analysis of educational texts. This can help personalize learning and provide instant feedback to students. Speech recognition systems like virtual assistants benefit from NLP to understand user queries accurately.

It processes the information and converts it into a format that a computer can understand. Mainly, it is a subfield of Artificial Intelligence (AI) that is about the interaction between computers and human languages. Social listening powered by AI tasks like NLP enables you to analyze thousands of social conversations in seconds to get the business intelligence you need. It gives you tangible, data-driven insights to build a brand strategy that outsmarts competitors, forges a stronger brand identity and builds meaningful audience connections to grow and flourish. NLP models face many challenges due to the complexity and diversity of natural language. Some of these challenges include ambiguity, variability, context-dependence, figurative language, domain-specificity, noise, and lack of labeled data.

Purdue University used the feature to filter their Smart Inbox and apply campaign tags to categorize outgoing posts and messages based on social campaigns. This helped them keep a pulse on campus conversations to maintain brand health and ensure they never missed an opportunity to interact with their audience. As a result, they were able to stay nimble and pivot their content strategy based on real-time trends derived from Sprout. This increased their content performance significantly, which resulted in higher organic reach. Individuals working in NLP may have a background in computer science, linguistics, or a related field.

“Most banks have internal compliance teams to help them deal with the maze of compliance requirements. AI cannot replace these teams, but it can help to speed up the process by leveraging deep learning and natural language processing (NLP) to review compliance requirements and improve decision-making. Even organizations with large budgets like national governments and global corporations are using data analysis tools, algorithms, and natural language processing. “Text analytics is a computational field that draws heavily from the machine learning and statistical modeling niches as well as the linguistics space. In this space, computers are used to analyze text in a way that is similar to a human’s reading comprehension.

We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails. Turns out, these recordings may be used for training purposes, if a customer is aggrieved, but most of the time, they go into the database for an NLP system to learn from and improve in the future. 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. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment.

With NLP spending expected to increase in 2023, now is the time to understand how to get the greatest value for your investment. 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. Spam filters are where it all started – they uncovered patterns of words or phrases that were linked to spam messages. You can foun additiona information about ai customer service and artificial intelligence and NLP. Since then, filters have been continuously upgraded to cover more use cases.

Example of Natural Language Processing for Author Identification

This opens the door for incredible insights to be unlocked on a scale that was previously inconceivable without massive amounts of manual intervention. One of the annoying consequences of not normalising spelling is that words like normalising/normalizing do not tend to be picked up as high frequency words if they are split between variants. For that reason we often have to use spelling and grammar normalisation tools.

This was so prevalent that many questioned if it would ever be possible to accurately translate text. Email filters are common NLP examples you can find online across most servers. Data analysis has come a long way in interpreting survey results, although the final challenge is making sense of open-ended responses and unstructured text. NLP, with the support of other AI disciplines, is working towards making these advanced analyses possible. Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes.

At the same time, we all are using NLP on a daily basis without even realizing it. A quick look at the beginner’s guide to natural language processing can help. Given a block of text, the algorithm counted the number of polarized words in the text; if there were more negative words than positive ones, the sentiment would be defined as negative. Depending on sentence structure, this approach could easily lead to bad results (for example, from sarcasm). Being able to create a shorter summary of longer text can be extremely useful given the time we have available and the massive amount of data we deal with daily.

NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text. Sentiment analysis has been used in finance to identify emerging trends which can indicate profitable trades. Natural language processing (NLP) is particularly useful in helping AI understand language contextually. As a result, data extraction from text-based documents becomes feasible, as does facilitating complex analytics processes such as sentiment analysis, voice recognition, topic modeling, entity recognition and chatbots.

And while applications like ChatGPT are built for interaction and text generation, their very nature as an LLM-based app imposes some serious limitations in their ability to ensure accurate, sourced information. Where a search engine returns results that are sourced and verifiable, ChatGPT does not cite sources and may even return information that is made up—i.e., hallucinations. At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans.

Statistical Language Models

Now that we’ve explored the basics of NLP, let’s look at some of the most popular applications of this technology. In Named Entity Recognition, we detect and categorize pronouns, names of people, organizations, places, and dates, among others, in a text document. NER systems can help filter valuable details from the text for different uses, e.g., information extraction, entity linking, and the development of knowledge graphs. Identifying and categorizing named entities such as persons, organizations, locations, dates, and more in a text document. Segmenting words into their constituent morphemes to understand their structure. Duplicate detection makes sure that you see a variety of search results by collating content re-published on multiple sites.

By following these steps, you’ll kickstart your NLP journey and establish a strong foundation of knowledge and experience. This will set you up for success as you continue to develop your skills and tackle increasingly complex NLP tasks. Additionally, a large amount of data and the quality of that data used can greatly impact the performance of NLP models. A model trained on high-quality data is more likely to produce accurate and reliable results.

The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check. Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically tagged as one with negative sentiment. Autocorrect can even change words based on typos so that the overall sentence’s meaning makes sense. These functionalities have the ability to learn and change based on your behavior.

Pre-trained Language Models have gained significant attention in recent years. These models are trained on vast amounts of text data and learn to understand language by capturing complex patterns and semantic relationships. The ability to understand and process human language has become increasingly important, leading to the development of Natural Language Processing (NLP) systems. These examples of natural language processing are the top 7 solutions for why should businesses use natural language processing and the list is never-ending. Hence, it is an example of why should businesses use natural language processing. These are the 12 most prominent natural language processing examples and there are many in the lines used in the healthcare domain, for aircraft maintenance, for trading, and a lot more.

These predictions help uncover trends in data that translate to actionable insights. These insights were also used to coach conversations across the social support team for stronger customer service. Plus, they were critical for the broader marketing and product teams to improve the product based on what customers wanted.

NLP customer service implementations are being valued more and more by organizations. Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post. It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one. These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP.

Recent advances in deep learning, particularly in the area of neural networks, have led to significant improvements in the performance of NLP systems. Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to tasks such as sentiment analysis and machine translation, achieving state-of-the-art results. 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. The primary goal of NLP is to empower computers to comprehend, interpret, and produce human language.

The sheer number of variables that need to be accounted for in order for a natural learning process application to be effective is beyond the scope of even the most skilled programmers. This is where machine learning AIs have served as an essential piece of natural language processing techniques. Using Waston Assistant, businesses can create natural language processing applications that can understand customer and employee languages while reverting back to a human-like conversation manner. Watson is one of the known natural language processing examples for businesses providing companies to explore NLP and the creation of chatbots and others that can facilitate human-computer interaction. Social media is one of the most important tools to gain what and how users are responding to a brand.

Top Techniques in Natural Language Processing

All these suggestions are provided using autocomplete that uses Natural Language Processing to guess what you want to ask. Search engines use their enormous data sets to analyze what their customers are probably typing when they enter particular words and suggest the most common possibilities. They use Natural Language Processing to make sense of these words and how they are interconnected to form different sentences.

Deep learning has been found to be highly accurate for sentiment analysis, with the downside that a significant training corpus is required to achieve accuracy. The deep neural network learns the structure of word sequences and the sentiment of each sequence. Given the variable nature of sentence length, an RNN is commonly used and can consider words as a sequence. A popular deep neural network architecture that implements recurrence is LSTM. PyTorch-NLPOpens a new window is another library for Python designed for the rapid prototyping of NLP.

This frees up human employees from routine first-tier requests, enabling them to handle escalated customer issues, which require more time and expertise. Thankfully, natural language processing can identify all topics and subtopics within a single interaction, with ‘root cause’ analysis that drives actionability. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks. Chatbots do all this by recognizing the intent of a user’s query and then presenting the most appropriate response. At the same time, NLP could offer a better and more sophisticated approach to using customer feedback surveys.

While it’s not exactly 100% accurate, it is still a great tool to convert text from one language to another. Google Translate and other translation tools as well as use Sequence to sequence modeling that is a technique in Natural Language Processing. It allows the algorithm to convert a sequence of words from one language to another which is translation.

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With recent advancements, it excels at writing news articles and generating code. What sets ChatGPT-3 apart is its ability to perform downstream tasks without needing fine-tuning, effectively managing statistical dependencies between different words. The model’s remarkable performance is attributed to its extensive training on over 175 billion parameters, drawing from a colossal 45 TB text corpus sourced from various internet sources. Natural language generation (NLG) is the process of generating human-like text based on the insights gained from NLP tasks. NLG can be used in chatbots, automatic report writing, and other applications. Parsing involves analyzing the grammatical structure of a sentence to understand the relationships between words.

The models could subsequently use the information to draw accurate predictions regarding the preferences of customers. Businesses can use product recommendation insights through personalized product pages or email campaigns targeted at specific groups of consumers. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models.

The top NLP examples in the field of consumer research would point to the capabilities of NLP for faster and more accurate analysis of customer feedback to understand customer sentiments for a brand, service, or product. NLP can also help you route the customer support tickets to the right person according to their content and topic. This way, you can save lots of valuable time by making sure that everyone in your customer service team is only receiving relevant support tickets. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. Things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. Autocomplete and predictive text are similar to search engines in that they predict things to say based on what you type, finishing the word or suggesting a relevant one.

• You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity.
• In her free time, you’ll often find her at museums and art galleries, or chilling at home watching war movies.
• Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume.
• Deep 6 AI developed a platform that uses machine learning, NLP and AI to improve clinical trial processes.
• 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.
• This was so prevalent that many questioned if it would ever be possible to accurately translate text.

Online translators are now powerful tools thanks to Natural Language Processing. 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.

This is so that machines can understand and interpret the human language to eventually understand human communication in a better way. Natural Language Processing is a cross among many different fields such as artificial intelligence, computational linguistics, human-computer interaction, etc. There are many different methods in NLP to understand human language which include statistical and machine learning methods.

QA systems process data to locate relevant information and provide accurate answers. Semantic search enables a computer to contextually interpret the intention of the user without depending on keywords. These algorithms work together with NER, NNs and knowledge graphs to provide remarkably accurate results. Semantic search powers applications such as search engines, smartphones and social intelligence tools like Sprout Social. NLP powers AI tools through topic clustering and sentiment analysis, enabling marketers to extract brand insights from social listening, reviews, surveys and other customer data for strategic decision-making.

• Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.
• With Akkio, we are able to build and deploy AI models in minutes, with no prior machine learning expertise or coding.” Sign up for a free trial of Akkio and see how NLP can help your business.
• The top NLP examples in the field of consumer research would point to the capabilities of NLP for faster and more accurate analysis of customer feedback to understand customer sentiments for a brand, service, or product.
• Predictive text on your smartphone or email, text summaries from ChatGPT and smart assistants like Alexa are all examples of NLP-powered applications.
• This technology is still evolving, but there are already many incredible ways natural language processing is used today.

This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. Let’s take farm supply brand Rural King as an example of this practice in action. The company offers free popcorn at its locations as part of the shopping experience. The investment in the snack is paying off with the “popcorn” keyword used in a positive sentiment in more than 2,400 reviews.

These tasks exploit the language’s inherent sequential order of words and sentences, allowing the model to capitalize on language structures at both the word and sentence levels. This design choice facilitates the model’s adaptability to varying levels of language understanding demanded by downstream tasks. OpenAI’s GPT-2 is an impressive language model showcasing autonomous learning skills. It can generate coherent paragraphs and achieve promising results in various tasks, making it a highly competitive model. ChatGPT-3 is a transformer-based NLP model renowned for its diverse capabilities, including translations, question answering, and more.