10 Examples of Natural Language Processing in Action

You’ve likely seen this application of natural language processing in several places. Whether it’s on your smartphone keyboard, search engine search bar, or when you’re writing an email, predictive text is fairly prominent. Natural language processing is one of the most promising fields within Artificial Intelligence, and it’s already present in many applications we use on a daily basis, from chatbots to search engines.

It helps to bring structure to something that is inherently unstructured, which can make for smarter software and even allow us to communicate better with other people. Here, we take a closer look at what natural language processing means, how it’s implemented, and how you can start learning some of the skills and knowledge you’ll need to work with this technology. We rely on it to navigate the world around us and communicate with others. Yet until recently, we’ve had to rely on purely text-based inputs and commands to interact with technology. Now, natural language processing is changing the way we talk with machines, as well as how they answer. Natural language generation, NLG for short, is a natural language processing task that consists of analyzing unstructured data and using it as an input to automatically create content.

A slightly more sophisticated technique for language identification is to assemble a list of N-grams, which are sequences of characters which have a characteristic frequency in each language. For example, the combination ch is common in English, Dutch, Spanish, German, French, and other languages. Natural language processing provides us with a Chat PG set of tools to automate this kind of task. When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it. If you’re currently collecting a lot of qualitative feedback, we’d love to help you glean actionable insights by applying NLP.

Statistical NLP, machine learning, and deep learning

This makes for fun experiments where individuals will share entire sentences made up entirely of predictive text on their phones. The results are surprisingly personal and enlightening; they’ve even been highlighted by several media outlets. For example, any company that collects customer feedback in free-form as complaints, social media posts or survey results like NPS, can use NLP to find actionable insights in this data. With its ability to process large amounts of data, NLP can inform manufacturers on how to improve production workflows, when to perform machine maintenance and what issues need to be fixed in products. And if companies need to find the best price for specific materials, natural language processing can review various websites and locate the optimal price. Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data.

We, as humans, perform natural language processing (NLP) considerably well, but even then, we are not perfect. We often misunderstand one thing for another, and we often interpret the same sentences or words differently. Semantic search, an area of natural language processing, can better understand the intent behind what people are searching (either by voice or text) and return more meaningful results based on it. Natural language processing is a branch of artificial intelligence (AI).

Predictive text and its cousin autocorrect have evolved a lot and now we have applications like Grammarly, which rely on natural language processing and machine learning. We also have Gmail’s Smart Compose which finishes your sentences for you as you type. Today most people have interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline and automate business operations, increase employee productivity, and simplify mission-critical business processes. Next, you’ll want to learn some of the fundamentals of artificial intelligence and machine learning, two concepts that are at the heart of natural language processing.

Now, this is the case when there is no exact match for the user’s query. If there is an exact match for the user query, then that result will be displayed first. Then, let’s suppose there are four descriptions available in our database. Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions.

Analytically speaking, punctuation marks are not that important for natural language processing. Therefore, in the next step, we will be removing such punctuation marks. In this article, we explore the basics of natural language processing (NLP) with code examples.

If you’re interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python’s natural language toolkit (NLTK) that I created. You can also check out my blog post about building neural networks with Keras where I train a neural network to perform sentiment analysis. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text.

We call it “Bag” of words because we discard the order of occurrences of words. A bag of words model converts the raw text into words, and it also counts the frequency for the words in the text. In summary, a bag of words is a collection of words that represent a sentence along with the word count where the order of occurrences is not relevant. It uses large amounts of data and tries to derive conclusions from it.

Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs. That actually nailed it but it could be a little more comprehensive. We resolve this issue by using Inverse Document Frequency, which is high if the word is rare and low if the word is common across the corpus. In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence. Companies nowadays have to process a lot of data and unstructured text. Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume.

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates. We’ve already explored the many uses of Python programming, and NLP is a field that often draws on the language. What’s more, Python has an extensive library (Natural Language Toolkit, NLTK) which can be used for NLP. If you want to learn more https://chat.openai.com/ about how and why conversational interfaces have developed, check out our introductory course. For example, MonkeyLearn offers a series of offers a series of no-code NLP tools that are ready for you to start using right away. If you want to integrate tools with your existing tools, most of these tools offer NLP APIs in Python (requiring you to enter a few lines of code) and integrations with apps you use every day.

NLP can help you leverage qualitative data from online surveys, product reviews, or social media posts, and get insights to improve your business. Read on to learn what natural language processing is, how NLP can make businesses more effective, and discover popular natural language processing techniques and examples. Finally, we’ll show you how to get started with easy-to-use NLP tools. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations. One example is smarter visual encodings, offering up the best visualization for the right task based on the semantics of the data. This opens up more opportunities for people to explore their data using natural language statements or question fragments made up of several keywords that can be interpreted and assigned a meaning.

Whether it’s through Siri, Alexa, Google Assistant or other similar technology, many of us use these NLP-powered devices. Since you don’t need to create a list of predefined tags or tag any data, it’s a good option for exploratory analysis, when you are not yet familiar with your data. NLP tools process data in real time, 24/7, and apply the same criteria to all your data, so you can ensure the results you receive are accurate – and not riddled with inconsistencies. All this business data contains a wealth of valuable insights, and NLP can quickly help businesses discover what those insights are. An NLP system can look for stopwords (small function words such as the, at, in) in a text, and compare with a list of known stopwords for many languages. The language with the most stopwords in the unknown text is identified as the language.

Rule-based NLP vs. Statistical NLP:

As well as the vocabulary, syntax, and grammar that make written sentences, there is also the phonetics, tones, accents, and diction of spoken languages. Natural language processing has been around for years but is often taken for granted. Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science.

One of the challenges of NLP is to produce accurate translations from one language into another. It’s a fairly established field of machine learning and one that has seen significant strides forward in recent years. The first thing to know about natural language processing is that there are several functions or tasks that make up the field.

As the technology advances, we can expect to see further applications of NLP across many different industries. Businesses are inundated with unstructured data, and it’s impossible for them to analyze and process all this data without the help of Natural Language Processing (NLP). Natural language processing can be used to improve customer experience in the form of chatbots and systems for triaging incoming sales enquiries and customer support requests. You can foun additiona information about ai customer service and artificial intelligence and NLP. However, there is still a lot of work to be done to improve the coverage of the world’s languages.

As shown above, all the punctuation marks from our text are excluded. Notice that the most used words are punctuation marks and stopwords. In the natural language example example above, we can see the entire text of our data is represented as sentences and also notice that the total number of sentences here is 9.

These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel. With sentiment analysis we want to determine the attitude (i.e. the sentiment) of a speaker or writer with respect to a document, interaction or event. Therefore it is a natural language processing problem where text needs to be understood in order to predict the underlying intent. The sentiment is mostly categorized into positive, negative and neutral categories. However, large amounts of information are often impossible to analyze manually.

That is why it generates results faster, but it is less accurate than lemmatization. In the code snippet below, many of the words after stemming did not end up being a recognizable dictionary word. Stemming normalizes the word by truncating the word to its stem word.

Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society. 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. As the name suggests, predictive text works by predicting what you are about to write.

The second “can” word at the end of the sentence is used to represent a container that holds food or liquid. NLP can be used for a wide variety of applications but it’s far from perfect. In fact, many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors, and other types of ambiguous statements. This means that NLP is mostly limited to unambiguous situations that don’t require a significant amount of interpretation.

This lets computers partly understand natural language the way humans do. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. Natural language processing is one of the most complex fields within artificial intelligence. But, trying your hand at NLP tasks like sentiment analysis or keyword extraction needn’t be so difficult. There are many online NLP tools that make language processing accessible to everyone, allowing you to analyze large volumes of data in a very simple and intuitive way.

Data scientists need to teach NLP tools to look beyond definitions and word order, to understand context, word ambiguities, and other complex concepts connected to human language. While there are many challenges in natural language processing, the benefits of NLP for businesses are huge making NLP a worthwhile investment. You would think that writing a spellchecker is as simple as assembling a list of all allowed words in a language, but the problem is far more complex than that. Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used.

Hence, from the examples above, we can see that language processing is not “deterministic” (the same language has the same interpretations), and something suitable to one person might not be suitable to another. Therefore, Natural Language Processing (NLP) has a non-deterministic approach. In other words, Natural Language Processing can be used to create a new intelligent system that can understand how humans understand and interpret language in different situations.

• However, there any many variations for smoothing out the values for large documents.
• Chatbots might be the first thing you think of (we’ll get to that in more detail soon).
• For example, NPS surveys are often used to measure customer satisfaction.
• Current systems are prone to bias and incoherence, and occasionally behave erratically.

We are going to use isalpha( ) method to separate the punctuation marks from the actual text. Also, we are going to make a new list called words_no_punc, which will store the words in lower case but exclude the punctuation marks. Next, we can see the entire text of our data is represented as words and also notice that the total number of words here is 144.

In this example, above, the results show that customers are highly satisfied with aspects like Ease of Use and Product UX (since most of these responses are from Promoters), while they’re not so happy with Product Features. Named Entity Recognition (NER) allows you to extract the names of people, companies, places, etc. from your data. However, it’s important to know what those challenges are before getting started with NLP. Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic. As an extension of the above problem, sometimes a text appears with an unknown author and we want to know who wrote it. After this problem appeared in so many of my projects, I wrote my own Python package called localspelling which allows a user to convert all text in a document to British or American, or to detect which variant is used in the document.

Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines.

Relationship extraction takes the named entities of NER and tries to identify the semantic relationships between them. This could mean, for example, finding out who is married to whom, that a person works for a specific company and so on. This problem can also be transformed into a classification problem and a machine learning model can be trained for every relationship type. The letters directly above the single words show the parts of speech for each word (noun, verb and determiner).

What Is Natural Language Understanding (NLU)?

Each area is driven by huge amounts of data, and the more that’s available, the better the results. Similarly, each can be used to provide insights, highlight patterns, and identify trends, both current and future. SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge. So for machines to understand natural language, it first needs to be transformed into something that they can interpret. Businesses in industries such as pharmaceuticals, legal, insurance, and scientific research can leverage the huge amounts of data which they have siloed, in order to overtake the competition. Natural language processing can help customers book tickets, track orders and even recommend similar products on e-commerce websites.

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI). It helps machines process and understand the human language so that they can automatically perform repetitive tasks. Examples include machine translation, summarization, ticket classification, and spell check. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines. Natural language processing shares many of these attributes, as it’s built on the same principles.

Over time, predictive text learns from you and the language you use to create a personal dictionary. 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. SpaCy is an open-source natural language processing Python library designed to be fast and production-ready. Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed-up their writing process and correct common typos.

NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. Recruiters and HR personnel can use natural language processing to sift through hundreds of resumes, picking out promising candidates based on keywords, education, skills and other criteria. In addition, NLP’s data analysis capabilities are ideal for reviewing employee surveys and quickly determining how employees feel about the workplace.

Yet the way we speak and write is very nuanced and often ambiguous, while computers are entirely logic-based, following the instructions they’re programmed to execute. This difference means that, traditionally, it’s hard for computers to understand human language. Natural language processing aims to improve the way computers understand human text and speech.

Typical purposes for developing and implementing a controlled natural language are to aid understanding by non-native speakers or to ease computer processing. An example of a widely-used controlled natural language is Simplified Technical English, which was originally developed for aerospace and avionics industry manuals. We all hear “this call may be recorded for training purposes,” but rarely do we wonder what that entails.

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. But a lot of the data floating around companies is in an unstructured format such as PDF documents, and this is where Power BI cannot help so easily. Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically.

How Does Natural Language Processing (NLP) Work?

Now, however, it can translate grammatically complex sentences without any problems. This is largely thanks to NLP mixed with ‘deep learning’ capability. Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences. Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether it be written, spoken, or even scribbled.

According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used.

If a particular word appears multiple times in a document, then it might have higher importance than the other words that appear fewer times (TF). At the same time, if a particular word appears many times in a document, but it is also present many times in some other documents, then maybe that word is frequent, so we cannot assign much importance to it. 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 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.

Natural Language Processing enables you to perform a variety of tasks, from classifying text and extracting relevant pieces of data, to translating text from one language to another and summarizing long pieces of content. There are more than 6,500 languages in the world, all of them with their own syntactic and semantic rules. The science of identifying authorship from unknown texts is called forensic stylometry. Every author has a characteristic fingerprint of their writing style – even if we are talking about word-processed documents and handwriting is not available. Natural Language Processing is what computers and smartphones use to understand our language, both spoken and written.

IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP. Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. By using Towards AI, you agree to our Privacy Policy, including our cookie policy. Named entity recognition can automatically scan entire articles and pull out some fundamental entities like people, organizations, places, date, time, money, and GPE discussed in them. However, what makes it different is that it finds the dictionary word instead of truncating the original word.

As shown above, the final graph has many useful words that help us understand what our sample data is about, showing how essential it is to perform data cleaning on NLP. Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. For instance, the sentence “The shop goes to the house” does not pass. Beginners in the field might want to start with the programming essentials with Python, while others may want to focus on the data analytics side of Python. As we explore in our open step on conversational interfaces, 1 in 5 homes across the UK contain a smart speaker, and interacting with these devices using our voices has become commonplace.

When you search on Google, many different NLP algorithms help you find things faster. 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 in making search work. The beauty of NLP is that it all happens without your needing to know how it works.

Imagine there’s a spike in negative comments about your brand on social media; sentiment analysis tools would be able to detect this immediately so you can take action before a bigger problem arises. Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the 1990s. To summarize, natural language processing in combination with deep learning, is all about vectors that represent words, phrases, etc. and to some degree their meanings.

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. Computers and machines are great at working with tabular data or spreadsheets. However, as human beings generally communicate in words and sentences, not in the form of tables. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it.

The machine learning model will look at the probability of which word will appear next, and make a suggestion based on that. Older forms of language translation rely on what’s known as rule-based machine translation, where vast amounts of grammar rules and dictionaries for both languages are required. More recent methods rely on statistical machine translation, which uses data from existing translations to inform future ones. Ultimately, NLP can help to produce better human-computer interactions, as well as provide detailed insights on intent and sentiment. These factors can benefit businesses, customers, and technology users. Yet with improvements in natural language processing, we can better interface with the technology that surrounds us.

AI is a field focused on machines simulating human intelligence, while NLP focuses specifically on understanding human language. Both are built on machine learning – the use of algorithms to teach machines how to automate tasks and learn from experience. One of the main reasons natural language processing is so critical to businesses is that it can be used to analyze large volumes of text data, like social media comments, customer support tickets, online reviews, news reports, and more.

Teams can also use data on customer purchases to inform what types of products to stock up on and when to replenish inventories. Now, imagine all the English words in the vocabulary with all their different fixations at the end of them. To store them all would require a huge database containing many words that actually have the same meaning. Popular algorithms for stemming include the Porter stemming algorithm from 1979, which still works well.

It is spoken by over 10 million people worldwide and is one of the two official languages of the Republic of Haiti. For further examples of how natural language processing can be used to your organisation’s efficiency and profitability please don’t hesitate to contact Fast Data Science. Natural language processing can be used for topic modelling, where a corpus of unstructured text can be converted to a set of topics. Key topic modelling algorithms include k-means and Latent Dirichlet Allocation.

As AI-powered devices and services become increasingly more intertwined with our daily lives and world, so too does the impact that NLP has on ensuring a seamless human-computer experience. The concept of natural language processing dates back further than you might think. As far back as the 1950s, experts have been looking for ways to program computers to perform language processing. However, it’s only been with the increase in computing power and the development of machine learning that the field has seen dramatic progress.

However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. Notice that the first description contains 2 out of 3 words from our user query, and the second description contains 1 word from the query. The third description also contains 1 word, and the forth description contains no words from the user query. As we can sense that the closest answer to our query will be description number two, as it contains the essential word “cute” from the user’s query, this is how TF-IDF calculates the value.