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What Is Natural Language Processing? - EWeek

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Natural language processing (NLP) is a branch of artificial intelligence (AI) that focuses on computers incorporating speech and text in a manner similar to humans understanding. This area of computer science relies on computational linguistics—typically based on statistical and mathematical methods—that model human language use.

NLP plays an increasingly prominent role in computing—and in the everyday lives of humans. Smart assistants such as Apple's Siri, Amazon's Alexa and Microsoft's Cortana are examples of systems that use NLP.

In addition, various other tools rely on natural language processing. Among them: navigation systems in automobiles; speech-to-text transcription systems such as Otter and Rev; chatbots; and voice recognition systems used for customer support. In fact, NLP appears in a rapidly expanding universe of applications, tools, systems and technologies.

In every instance, the goal is to simplify the interface between humans and machines. In many cases, the ability to speak to a system or have it recognize written input is the simplest and most straightforward way to accomplish a task.

While computers cannot "understand" language the same way humans do, natural language technologies are increasingly adept at recognizing the context and meaning of phrases and words and transforming them into appropriate responses—and actions.

Also see: Top Natural Language Processing Companies

Natural Language Processing: A Brief History

The idea of machines understanding human speech extends back to early science fiction novels. However, the field of natural language processing began to take shape in the 1950s, after computing pioneer Alan Turing published an article titled "Computing Machinery and Intelligence." It introduced the Turing Test, which provided a basic way to gauge a computer's natural language abilities.

During the ensuing decade, researchers experimented with computers translating novels and other documents across spoken languages, though the process was extremely slow and prone to errors. In the 1960s, MIT professor Joseph Weizenbaum developed ELIZA, which mimicked human speech patterns remarkably well. Over the next quarter century, the field continued to evolve. As computing systems became more powerful in the 1990s, researchers began to achieve notable advances using statistical modeling methods.

Dictation and language translation software began to mature in the 1990s. However, early systems required training, they were slow, cumbersome to use and prone to errors. It wasn't until the introduction of supervised and unsupervised machine learning in the early 2000s, and then the introduction of neural nets around 2010, that the field began to advance in a significant way.

With these developments, deep learning systems were able to digest massive volumes of text and other data and process it using far more advanced language modeling methods. The resulting algorithms had become far more accurate and utilitarian.

Also see: Top AI Software 

How Does Natural Language Processing Work?

Early NLP systems relied on hard coded rules, dictionary lookups and statistical methods to do their work. They frequently supported basic decision-tree models. Eventually, machine learning automated tasks while improving results.

Today's natural language processing frameworks use far more advanced—and precise—language modeling techniques. Most of these methods rely on convolutional neural networks (CNNs) to study language patterns and develop probability-based outcomes.

For example, a method called word vectors applies complex mathematical models to weight and relate words, phrases and constructs. Another method called Recognizing Textual Entailment (RTE), classifies relationships of words and sentences through the lens of entailment, contradiction, or neutrality. For instance, the premise "a dog has paws" entails that "dogs have legs" but contradicts "dogs have wings" while remaining neutral to "all dogs are happy."

A key part of NLP is word embedding. It refers to establishing numerical weightings for words in specific context. The process is necessary because many words and phrases can mean different things in different meanings or contexts (go to a club, belong to a club or swing a club). Words can also be pronounced the same way but mean different things (through, threw or witch, which). There's also a need to understand idiomatic phrases that do not make sense literally, such as "You are the apple of my eye" or "it doesn't cut the mustard."

Today's models are trained on enormous volumes of language data—in some cases several hundred gigabytes of books, magazines articles, websites, technical manuals, emails, song lyrics, stage plays, scripts and publicly available sources such as Wikipedia. As the deep learning system parse through millions or even billions of combinations—relying on hundreds of thousands of CPU or GPU cores—they analyze patterns, connect the dots and learn semantic properties of words and phrases.

It's also often necessary to refine natural language processing systems for specific tasks, such as a chatbot or a smart speaker. But even after this takes place, a natural language processing system may not always work as billed. Even the best NLPs make errors. They can encounter problems when people misspell or mispronounce words and they sometimes misunderstand intent and translate phrases incorrectly. In some cases, these errors can be glaring—or even catastrophic.

Today, prominent natural language models are available under licensing models. These include the OpenAI codex, LaMDA by Google, IBM Watson and software development tools such as CodeWhisperer and CoPilot. In addition, some organizations build their own proprietary models.

How is Natural Language Processing Used?

There are a growing array of uses for natural language processing. These include:

Conversational AI. The ability of computers to recognize words introduces a variety of applications and tools. Personal assistants like Siri, Alexa and Microsoft Cortana are prominent examples of conversational AI. They allow humans to make a call from a mobile phone while driving or switch lights on or off in a smart home. Increasingly, these systems understand intent and act accordingly. For example, chatbots can respond to human voice or text input with responses that seem as if they came from another person. What's more, these systems use machine learning to constantly improve.

Machine translation. There's a growing use of NLP for machine translation tasks. These include language translations that replace words in one language for another (English to Spanish or French to Japanese, for example). Google Translate and DeepL are examples of this technology. But machine translation can also take other forms. For example, NLP can convert spoken words—either in the form of a recording or live dictation—into subtitles on a TV show or a transcript from a Zoom or Microsoft Teams meeting. Yet while these systems are increasingly accurate and valuable, they continue to generate some errors.

Sentiment analysis. NLP has the ability to parse through unstructured data—social media analysis is a prime example—extract common word and phrasing patterns and transform this data into a guidepost for how social media and online conversations are trending. This capability is also valuable for understanding product reviews, the effectiveness of advertising campaigns, how people are reacting to news and other events, and various other purposes. Sentiment analysis finds things that might otherwise evade human detection.

Content analysis. Another use case for NLP is making sense of complex systems. For example, the technology can digest huge volumes of text data and research databases and create summaries or abstracts that relate to the most pertinent and salient content. Similarly, content analysis can be used for cybersecurity, including spam detection. These systems can reduce or eliminate the need for manual human involvement.

Text and image generation. A rapidly emerging part of natural language processing focuses on text, image and even music generation. Already, some news organizations produce short articles using natural language processing. Meanwhile, OpenAI has developed a tool that generates text and computer code through a natural language interface. Another OpenAI tool, dubbed Dall-E-2, creates high quality images through an NLP interface. Type the words "black cat under a stairway" and an image appears. GitHub Copilot and Amazon CodeWhisperer can auto-complete and auto-generate computer code through natural language.

Also see: Top Data Visualization Tools 

NLP Business Use Cases

The use of NLP is increasingly common in the business world. Among the top use cases:

Chatbots and voice interaction systems. Retailers, health care providers and others increasingly rely on chatbots to interact with customers, answer basic questions and route customers to other online resources. These systems can also connect a customer to a live agent, when necessary. Voice systems allow customers to verbally say what they need rather than push buttons on the phone.

Transcription. As organizations shift to virtual meetings on Zoom and Microsoft Teams, there's often a need for a transcript of the conversation. Services such as Otter and Rev deliver highly accurate transcripts—and they're often able to understand foreign accents better than humans. In addition, journalists, attorneys, medical professionals and others require transcripts of audio recordings. NLP can deliver results from dictation and recordings within seconds or minutes.

International translation. NLP has revolutionized interactions between businesses in different countries. While the need for translators hasn't disappeared, it's now easy to convert documents from one language to another. This has simplified interactions and business processes for global companies while simplifying global trade.

Scoring systems. Natural language is used by financial institutions, insurance companies and others to extract elements and analyze documents, data, claims and other text-based resources. The same technology can also aid in fraud detection, financial auditing, resume evaluations and spam detection. In fact, the latter represents a type of supervised machine learning that connects to NLP.

Market intelligence and sentiment analysis. Marketers and others increasingly rely on NLP to deliver market intelligence and sentiment trends. Semantic engines scrape content from blogs, news sites, social media sources and other sites in order to detect trends, attitudes and actual behaviors. Similarly, NLP can help organizations understand website behavior, such as search terms that identify common problems and how people use an e-commerce site. This data can lead to design and usability changes.

Software development. A growing trend is the use of natural language for software coding. Low-code and no-code environments can transform spoken and written requests into actual lines of software code. Systems such as Amazon's CodeWhisperer and GitHub's CoPilot include predictive capabilities that autofill code in much the same way that Google Mail predicts what a person will type next. They also can pull information from an integrated development environment (IDE) and produce several lines of code at a time.

Text and image generation. The OpenAI codex can generate entire documents, based a basic request. This makes it possible to generate poems, articles and other text. Open AI's DALL-E 2 generates photorealistic images and art through natural language input. This can aid designers, artists and others.

Also see: Best Data Analytics Tools 

What Ethical Concerns Exist for NLP?

Concerns about natural language processing are heavily centered on the accuracy of models and ensuring that bias doesn't occur. Many of these deep learning algorithms are so-called "black boxes," meaning that there's no way to understand how the underlying model works and whether it is free of biases that could affect critical decisions about lending, healthcare and more.

There is also debate about whether these systems are "sentient." The question of whether AI can actually think and feel like a human has been expressed in films such as 2001: A Space Odyssey and Star Wars. It also reappeared in 2022, when former Google data scientist Blake Lemoine published human-to-machine discussions with LaMDA. Lemoine claimed that the system had gained sentience. However, numerous linguistics experts and computer scientists countered that a silicon-based system cannot think and feel the way humans do. It merely parrots language in a highly convincing way.

In fact, researchers who have experimented with NLP systems have been able to generate egregious and obvious errors by inputting certain words and phrases. Getting to 100% accuracy in NLP is nearly impossible because of the nearly infinite number of word and conceptual combinations in any given language.

Another issue is ownership of content—especially when copyrighted material is fed into the deep learning model. Because many of these systems are built from publicly available sources scraped from the Internet, questions can arise about who actually owns the model or material, or whether contributors should be compensated. This has so far resulted in a handful of lawsuits along with broader ethical questions about how models should be developed and trained.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning

What Role Will NLP Play in the Future?

There's no question that natural language processing will play a prominent role in future business and personal interactions. Personal assistants, chatbots and other tools will continue to advance. This will likely translate into systems that understand more complex language patterns and deliver automated but accurate technical support or instructions for assembling or repairing a product.

NLP will also lead to more advanced analysis of medical data. For example, a doctor might input patient symptoms and a database using NLP would cross-check them with the latest medical literature. Or a consumer might visit a travel site and say where she wants to go on vacation and what she wants to do. The site would then deliver highly customized suggestions and recommendations, based on data from past trips and saved preferences.

For now, business leaders should follow the natural language processing space—and continue to explore how the technology can improve products, tools, systems and services. The ability for humans to interact with machines on their own terms simplifies many tasks. It also adds value to business relationships.

Also see: The Future of Artificial Intelligence

5 Natural Language Processing Libraries To Use - Cointelegraph

Natural language processing (NLP) is important because it enables machines to understand, interpret and generate human language, which is the primary means of communication between people. By using NLP, machines can analyze and make sense of large amounts of unstructured textual data, improving their ability to assist humans in various tasks, such as customer service, content creation and decision-making.

Additionally, NLP can help bridge language barriers, improve accessibility for individuals with disabilities, and support research in various fields, such as linguistics, psychology and social sciences.

Here are five NLP libraries that can be used for various purposes, as discussed below.

NLTK (Natural Language Toolkit)

One of the most widely used programming languages for NLP is Python, which has a rich ecosystem of libraries and tools for NLP, including the NLTK. Python's popularity in the data science and machine learning communities, combined with the ease of use and extensive documentation of NLTK, has made it a go-to choice for many NLP projects.

NLTK is a widely used NLP library in Python. It offers NLP machine-learning capabilities for tokenization, stemming, tagging and parsing. NLTK is great for beginners and is used in many academic courses on NLP.

Tokenization is the process of dividing a text into more manageable pieces, like specific words, phrases or sentences. Tokenization aims to give the text a structure that makes programmatic analysis and manipulation easier. A frequent pre-processing step in NLP applications, such as text categorization or sentiment analysis, is tokenization.

Words are derived from their base or root form through the process of stemming. For instance, "run" is the root of the terms "running," "runner," and "run." Tagging involves identifying each word's part of speech (POS) within a document, such as a noun, verb, adjective, etc.. In many NLP applications, such as text analysis or machine translation, where knowing the grammatical structure of a phrase is critical, POS tagging is a crucial step.

Parsing is the process of analyzing the grammatical structure of a sentence to identify the relationships between the words. Parsing involves breaking down a sentence into constituent parts, such as subject, object, verb, etc. Parsing is a crucial step in many NLP tasks, such as machine translation or text-to-speech conversion, where understanding the syntax of a sentence is important.

Related: How to improve your coding skills using ChatGPT?

SpaCy

SpaCy is a fast and efficient NLP library for Python. It is designed to be easy to use and provides tools for entity recognition, part-of-speech tagging, dependency parsing and more. SpaCy is widely used in the industry for its speed and accuracy.

Dependency parsing is a natural language processing technique that examines the grammatical structure of a phrase by determining the relationships between words in terms of their syntactic and semantic dependencies, and then building a parse tree that captures these relationships.

Stanford CoreNLP

Stanford CoreNLP is a Java-based NLP library that provides tools for a variety of NLP tasks, such as sentiment analysis, named entity recognition, dependency parsing and more. It is known for its accuracy and is used by many organizations.

Sentiment analysis is the process of analyzing and determining the subjective tone or attitude of a text, while named entity recognition is the process of identifying and extracting named entities, such as names, locations and organizations, from a text.

Gensim

Gensim is an open-source library for topic modeling, document similarity analysis and other NLP tasks. It provides tools for algorithms such as latent dirichlet allocation (LDA) and word2vec for generating word embeddings.

LDA is a probabilistic model used for topic modeling, where it identifies the underlying topics in a set of documents. Word2vec is a neural network-based model that learns to map words to vectors, enabling semantic analysis and similarity comparisons between words.

TensorFlow

TensorFlow is a popular machine-learning library that can also be used for NLP tasks. It provides tools for building neural networks for tasks such as text classification, sentiment analysis and machine translation. TensorFlow is widely used in industry and has a large support community.

Classifying text into predetermined groups or classes is known as text classification. Sentiment analysis examines a text's subjective tone to ascertain the author's attitude or feelings. Machines translate text from one language into another. While all use natural language processing techniques, their objectives are distinct.

Can NLP libraries and blockchain be used together?

NLP libraries and blockchain are two distinct technologies, but they can be used together in various ways. For instance, text-based content on blockchain platforms, such as smart contracts and transaction records, can be analyzed and understood using NLP approaches.

NLP can also be applied to creating natural language interfaces for blockchain applications, allowing users to communicate with the system using everyday language. The integrity and privacy of user data can be guaranteed by using blockchain to protect and validate NLP-based apps, such as chatbots or sentiment analysis tools.

Related: Data protection in AI chatting: Does ChatGPT comply with GDPR standards?

How Natural Language Processing Is Revolutionizing Business ... - Forbes

As a VP of Delivery at Intellias, Roman advises expertise to help businesses orchestrate their best products and services.

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This year, all eyes will be on natural language processing (NLP). With OpenAI's GPT-4 and Meta's LLaMA out, the race for the best AI-powered NLP tech is officially on. So what hides behind the hype? What does NLP have to offer for global businesses? And do human workers have to worry about losing their jobs?

From my perspective, NLP technology is certainly going to revolutionize business operations and productivity. Here's why.

NLP and business operations are a perfect match.

Today, many companies look closely at NLP solutions primarily based on the potential for cost savings. The technology has proven to be useful in saving resources such as time, money and human effort. For instance, IBM states that their NLP solutions can reduce time spent on information-gathering tasks by 50% (pg. 2).

Of course, how much NLP technology can save for a particular company will vary depending on the specific circumstances and context. In general, an NLP application can be useful in different areas of business operations, such as:

• Automating Routine Tasks: Take answering customer inquiries and processing transactions, for instance. An NLP solution can reduce the need for human involvement and save the company money on labor costs. According to Accenture, "40% of all working hours can be impacted by large language models (LLMs) like GPT-4."

• Structuring Company Data: An AI-powered NLP app can go through large volumes of text and analyze it on demand.

• Improving Customer Service And Supporting Efficiency: An NLP app can handle more customer inquiries in less time. It can improve the overall productivity of customer service teams.

• Improving Customer Satisfaction And Engagement: Thanks to high interactivity and 24/7 availability, research shows that LLMs could be useful in handling about 70% of complicated customer service communication (pg. 7).

• Conducting Dynamic Onboarding And Training: NLP solutions can train workers in an efficient and interactive way.

• Reducing Human Errors: NLP can save money by providing accurate and consistent responses and therefore reduce the need for costly corrections and rework.

Let's take a look at one of the possible applications of NLP. Recently, at my company, we built a conversational virtual teaching assistant for a global car manufacturer. The NLP app helps sales teams strengthen their product knowledge.

The chatbot understands questions and provides instant replies. The bot can also show a video, a photograph or a slide deck. Through this interactive training, a salesperson learns how to quickly and accurately consult customers on any questions about the product.

Here is the roadmap for successful NLP bot cooperation.

Let's take ChatGPT, for example. Here's how to use it properly for business operations.

1. Define your business goals before the implementation. This will help you choose the right provider and solution that align with your business objectives.

2. Choose a reputable provider that has a track record of successful ChatGPT deployments and can provide references with case studies.

3. Evaluate the solution's capabilities to ensure it can handle your business needs. Consider factors such as the solution's accuracy, speed, scalability and customization options.

4. Plan for deployment and maintenance. Make sure you have a plan for integrating the solution into your existing systems, training your team on how to use it and providing ongoing support and maintenance.

5. Test ChatGPT before deployment with a small group of customers or employees to identify any issues or bugs.

6. Constantly train ChatGPT to improve its performance and accuracy.

7. Address ethical considerations, such as potential bias in training data or lack of transparency.

8. Ensure data privacy and security to protect your customers' personal information and your business reputation.

9. Continuously monitor and evaluate the performance of ChatGPT to ensure it's meeting your business goals. Use performance metrics such as accuracy, response time and customer satisfaction to evaluate the solution's effectiveness.

10. Improve and innovate with ChatGPT to stay ahead of the competition. Explore new use cases and applications, and consider integrating new technologies.

The human touch is still important.

Despite the increasing sophistication of NLP solutions, there are situations in which human contact will remain equally important. For example, NLP solutions may not be able to provide accurate emotional support to customers who are upset or distressed. Sure, the technology can detect the emotion or intent behind a customer's text (i.E., perform sentiment analysis). But the response an NLP app generates can't yet compare in empathy to personal human contact.

In addition, certain situations are generally better suited to human workers, such as creative problem-solving and decision making. Judging from my experience, NLP technology can handle routine tasks and provide basic information really well. But it's not yet advanced enough to fully replace human workers' critical thinking.

So, no worries, NLP tech won't completely replace human interaction, expertise and experience—at least in the near future. Instead, for now, NLP technology can accompany human workers and assist them with certain tasks.

Beware: The future is here.

Everyone knows it; advanced NLP solutions are here to stay. They've already proven to be useful and worthy of investment. NLP apps help reduce the costs of conducting business operations. In addition, they drive customer satisfaction and grow revenue. It's no wonder that today, businesses go after NLP so obsessively.

Now, companies are in a position where the technology adoption becomes not just a small internal operations improvement but a matter of survival—because there's always a risk of getting overrun by your competitors.

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