What is natural language processing?
Natural Language Processing (NLP) is a tool that uses computer science, AI concepts, linguistics, and data to allow computers to understand human language – both written and verbal. It converts written or spoken words into data and makes it possible for devices to communicate with each other. These connected devices have changed our world – think of the smart home, for example – allowing us to use voice-based or text-based solutions to complete actions, often from a distance. Verbal command capability and chatbots (query management) have joined the scene, along with climate control devices (thermostats) and home monitoring devices (security systems). We may not be aware that the response to our inquiry is generated by an algorithm, the driving force behind NLP, but it often is. NLP offers the ability to program computers to analyze vast amounts of data from natural language. It allows us to use data from chatbots, social media posts, documents, and website pages. This tool is finding its way into marketing as well, and that’s what we’re going to discuss. Read on:The importance of natural language processing
NLP allows machines to understand human language. It bridges the human-to-machine gap by using Artificial Intelligence (AI) to process text and the spoken word. It’s a form of AI that manipulates and interprets human language using computational linguistics (the parsing out of parts of speech and words into data). NLP consists of two branches. Natural Language Understanding (NLU) extracts the meanings of words by studying the relationships between them. And Natural Language Generation (NLG) transforms data into understandable language, writing sentences and even paragraphs that are appropriate, well-constructed, and often personalized. It allows computers to create responses for chatbots and virtual assistants, write subject lines for emails, and even compose advertising copy and marketing tools. Here’s how to think about it: NLU focuses on your computer’s ability to read and listen (speech-to-text processing), and NLG allows it to write and speak (text-to-speech). Both are part of NLP. And Natural Language Processing applications are everywhere. Intelligent Personal Assistants (IPAs) answer customer questions. Voice assistants like Siri respond to commands. Marketers use it to create custom content, personalize or push specific promotions, and personalize offerings to the taste of a particular shopper. Auto-complete and auto-correct functions in texting either help you or drive you nuts. Machine translation tools clue you into words from other languages. Even brick-and-mortar stores can take advantage by customizing individual stores’ landing pages to show local hours of operation, addresses, directions, and additional information.How does natural language processing work?
I hear you asking: How in the world can a machine understand the spoken or written word? Well, it’s all about linguistic analysis. Let me explain: Natural Language Processing allows a computer to translate spoken or written words into data, including all of our colloquialisms, regionalisms, misspellings, and abbreviations. It’s astonishing, really, that this can be done, given all of our personalizations of speech. This is where computational linguistics comes into play. Programs can break down any text into three parts: its semantic information, syntactic information, and context information. The computer deals with each piece separately. Let’s define those terms.Semantic information
This is the actual meaning of a specific word. You can interpret a sentence like “He is enjoying the date” in different ways, depending on the meaning of “date.” Is the gentleman out on the town with a friend or chewing fruit from a palm tree? Knowing which form of the word “date” to use – knowing the relevant definition in this specific instance – is critical in understanding the meaning of that sentence.Syntax information
This refers to the sentence structure. We’ve moved from the word level to the phrase level. “Sarah died peacefully with her family on September 4.” Oh my – how many people died? The whole family, or just Sarah? That’s an important distinction! How can a computer figure that out?Context information
The relationship of words, phrases, or sentences to each other is the key here. How should it be understood if someone says, “Man, that’s hot!” Is the item’s temperature high? Or is the speaker describing something fashionable or something to be desired? So how can a machine be taught to analyze these distinctions? It’s not hard to teach a system to understand the basic rules and patterns of a language – that’s the science called linguistic computation. It just takes a lot of time. And (as we saw in the “hot” example above), the rules can’t always keep up with the evolution and revisions of language. The earliest text-mining systems were based entirely on patterns and rules, but as NLP and machine learning evolved, hybrid machine learning grew. Hybrid machine learning uses those same rules and patterns in both supervised and unsupervised models, and there are several different versions, using low-, mid-, and high-level text functioning. At the low level are the first processes that consider any input text. They turn the unstructured text into data. At the middle level are the text analytics that extract the content (Who’s speaking? What’s being said? What is being talked about?). At the high level is the sentiment analysis. Linguistic analysis is complicated — and it can be messy. As we’ve seen, the meanings of words change with the speaker’s intent and the listener’s expectations. Machine learning and NLP offer solid solutions for analyzing words, but each system must be tuned or trained to match the user’s needs.
Natural Language Understanding and Natural Language Generation are two subsets of Natural Language Processing.
Growing from GPT-3 to GPT-4
OpenAI, an AI research and deployment company, has made significant advancements in the field. Its GPT-1 and GPT-2, the first and second versions of the Generative Pre-Trained Transformer, were the original forays. GPT-3 is the current machine learning model. It can:- Use internet data to generate text.
- Take a small bit of input text and generate a large amount of sophisticated machine-generated text.
- Analyze and input piece of language and predict what the writer or speaker meant to infer.
How does Google use NLP?
Let’s talk about BERT (Bidirectional Encoder Representations from Transformers), an artificial language model of NLP used by businesses like Microsoft, Google, IBM, AWS, and Baidu, across various applications. Before using BERT, Google had been using other models to understand human language, but BERT allows it to reach beyond just the specific word to learn context. BERT determines a word’s context by considering all the surrounding words, not just the ones immediately before or after it. That capability strengthens Google’s user-friendliness and its ability to understand and respond to queries. Google reports that it uses NLP “at scale, across languages, and across domains” as it trains its systems to use it in searches, apps, mobile uses, and Translate. Google also reports that BERT performs well in its many uses on Google’s platforms. BERT has been fed an astonishing amount of data. Some sources estimate that it has been fed, or trained with, as many as 2.5 billion words. Having seen this extensive variety of terms, it can even begin to understand new words. Its continued application on large datasets has helped it to understand even small datasets. This allows Google to give relevant results even when a search query is poorly worded or misspelled.It recognizes “keywordese”
BERT even helps when Google users, trying to “speak computer,” resort to using “keywordese.” It can untangle complex syntax and determine the semantics of a request. It can account for a word’s context, determining whether it’s a noun, verb, or adjective. For example, it can use context to understand the difference in meaning between “the game’s result was a tie,” “she needs to tie back her hair,” and “wearing a tie to the prom is just the right thing to do.” Because BERT is open-source, researchers can grow and strengthen its performance. Its applications are so varied that it has been able to revolutionize the use of NLP. Its creators at Google developed BERT to optimize its search accuracy, reaching into areas like chatbots and summarization. Google reports that BERT assists in one in 10 of its searches in U.S. English and will continue into more languages over time.Content marketing and natural language processing
Natural language processing offers many applications for the digital marketing world. It can be a vital component of a content marketing plan, given its many uses. Highlights include:- Analyzing content for sentiment
- Helping to determine which keywords will be most relevant
- Writing product descriptions for digital commerce sites
- Helping to develop a marketing strategy by assessing the content of a particular client or by performing a content audit
- Refining chatbot functions to enable the gathering of solid leads.
Consider content sentiment and content analysis
How can a computer determine a customer’s sentiment? Well, it’s a two-pronged process that can use AI for both content analysis and sentiment analysis. Content analysis is the objective and quantitative assessment of a text-based, visual, or aural message. Researchers analyze each message scientifically. On the other hand, sentiment analysis is the science of interpreting and classifying a user’s emotions about a specific brand, product, or service by using text analysis. (Generally, the analysis determines whether the sentiment is positive, negative, or neutral.) The power of the internet and its reach into our lives has offered a vast amount of analyzable content gleaned from blogs, social media, YouTube use, and websites. The software can recognize patterns without human input.Determine your keywords
For the strongest search engine optimization (SEO), the right keywords are essential. NLP tools can help you choose keywords that increase your site’s traffic and attract just the right audience. Taking the time to find the most effective keywords will help your bottom line. Here’s how to do it:- Consider your customer. Pick just one – yourself. Picture yourself approaching your site for the first time. What might your questions be? Next, ask friends or new hires at your company what their search criteria might be. Ask your customers what they’ve used. Form a picture of your target customer’s probable search words.
- Consider the keywords used by your competitors. Visit their sites by using keywords that drive you there. Study the sites to see which words are used most frequently. Do they also apply to your business, service, or product? If so, add them to your list.
- Learn about long-tail keywords. These are the more specific phrases, often comprising three or more words, that searchers often use when they intend to take action. So, instead of just “sneakers,” a searcher might input “blue sneakers women size 7.” If you’ve got them, your business will show up somewhere.
- Add research tools to your keyword toolbox. Google Ads is a robust tool that can help you learn more about keyword trending and volume, search results, similar keywords, and even keyword competition.
- Finally, consider your findings. In this exercise, you will find search terms that your customers, competitors, and potential customers use to find the product or service you offer. Use those keywords across your business platforms: on your website, on social media, and in blogs. They’ll make it easier for potential customers to find you.
