Introduction to SEO: How Does Google Understand Human Language?

How Computers Understand Human Language

How Computers Understand Human Language

Language is an extraordinary human ability that enables us to communicate our thoughts, feelings, and ideas. A complex system of symbols, words, and grammar allows us to convey meaning. However, understanding human language can be incredibly challenging for computers operating on logical principles and mathematical calculations. Despite this, computers have made significant progress in comprehending and interpreting human language in recent years. Through advanced algorithms, computational linguistics, and machine learning, computers can now understand the nuances and complexities of human language, opening up a world of possibilities for human-computer interaction, natural language processing, and artificial intelligence. This article will delve into how computers understand human language and explore the techniques and technologies that make this remarkable achievement possible.

What is NLP?

A branch of computational linguistics and artificial intelligence called natural language processing (NLP) studies how computers and human (natural) languages interact. NLP allows computers to comprehend, interpret, and produce human language, opening up a world of applications. The creation of algorithms and models that can automatically process and analyze enormous volumes of data about human language and generate insightful results is the aim of natural language processing (NLP). It is a multidisciplinary discipline integrating computer science, linguistics, and psychology methods to create algorithms that can comprehend and analyze human language.

How Computers Understand Human Language

How does NLP work to understand Humans?

Thanks to NLP, computers can now understand natural language in the same manner humans can. Whether the language is spoken or written, natural language processing uses artificial intelligence to take in real-world input, analyze it, and make sense of it for a computer as individuals have many sensors, such as ears to hear and eyes to see, computers to have software to read and microphones to record audio. Furthermore, machines have software to process information, just as individuals have brains to do the same. The input is converted into code that the computer can understand during processing.

There are two phases to natural language processing:

  1. Data processing

Tokenization: The technique of dividing text into manageable chunks is known as tokenization.

Stop word removal: Only the distinctive words that provide the most information about the text remain after standard terms are eliminated from a manuscript.

Words are processed by reducing them to their root forms, known as lemmatization or stemming.

Section-of-speech labeling: Part-of-speech tagging is the process of labeling words based on the part of speech they belong to, such as nouns, verbs, and adjectives.

  1. Algorithm Creation

After the data has been preprocessed, an algorithm is developed to process it. While there are many other techniques for processing natural language, the two most popular ones are as follows:

Rules-based system: This system makes use of well-crafted language rules. This approach is still in use in the early days of natural language processing.

The machine learning system: Machine learning algorithms make use of statistical methods. They use training data given to them to learn how to execute tasks, and as additional data is processed, they modify their methods. Using a blend of machine learning, deep learning, and neural networks, natural language processing algorithms improve their rules via iterative processing and learning.

Can Google read?

What strategies do computers use to understand human language?

Computers use different strategies to understand human language, depending on the approach and the task. Some of the common strategies are:

  • Rule-basedIn this approach, the language data—such as grammar, syntax, or semantics—is subjected to predetermined rules or patterns. For instance, a rule-based part-of-speech tagging system may classify a word according to its suffix or sentence structure. Although rule-based systems are frequently straightforward to use and comprehend, their applicability may be restricted by the richness and diversity of natural language.

  • StatisticalIn this approach, many linguistic data, such as text or voice corpora, are used to train mathematical models and algorithms. The probability and patterns of language usage may be captured by statistical algorithms, which can also be made to adapt to new data or domains. For instance, a statistical voice recognition system may use a hidden Markov model to determine the most likely word sequence given a sound sequence. Although statistical systems might be more error- and noise-prone than rule-based systems, they are frequently more scalable and reliable.

  • Neural network-based: In this approach, linguistic data is generated and learned via artificial neural networks, which are computer models modeled after the composition and operation of real neurons. To accomplish a variety of natural language processing tasks, neural network systems can employ a variety of topologies and methodologies, including transformers, generative adversarial networks, attention mechanisms, convolutional neural networks, and recurrent neural networks. For instance, a neural network system may use a transformer model for natural language production to generate cohesive and fluid text in response to a particular input or environment. While neural network systems might be more complex to train and understand than statistical systems, they are frequently more robust and adaptable.

What is the purpose of it?

The following are the prominent NLP use cases:

Text Classification: Text may be categorized using natural language processing (NLP). Sorting emails into spam and non-spam categories is a typical illustration of that.

Sentiment analysis on social media: Natural language processing (NLP) has emerged as a crucial commercial tool for revealing hidden data insights from social media platforms. Sentiment analysis is a tool that businesses may use to understand better consumer attitudes and feelings towards events, promotions, and goods. It can also be used to analyze the language used in social media postings, replies, reviews, and other types of content.

Machine Translation: Natural Language Processing (NLP) translates text across German and English.

Named Entity Recognition: NLP is used to locate and retrieve certain entities—people, locations, and organizations—from text.

Spam detection: Although it may not seem like an NLP application, the most effective spam detection systems leverage text categorization features of NLP to search emails for terms that frequently point to spam or phishing. These warning signs may include misspelled business names, excessive use of financial terminology, threatening rhetoric, typical poor grammar, improper hurry, and more. One of the few NLP issues that specialists believe to be "mostly solved" is spam detection, despite your contentions that this doesn't reflect your email experience.

Question Answering: Natural language processing (NLP) creates systems that can respond to inquiries in natural language.

Chatbots and virtual agents: Chatbots, like Apple's Siri and Amazon's Alexa, employ natural language generation and speech recognition to identify patterns in voice requests and provide relevant actions or remarks. When a user types text, chatbots respond with the same magic. Over time, the most proficient ones acquire the ability to identify contextual cues related to human requests and utilize them to deliver ever-superior replies or possibilities. The capacity to answer our queries with pertinent, helpful, and original responses in their own words is the next improvement for these apps, whether or not it is anticipated.

Text Summarization: Natural Language Processing (NLP) automatically produces a synopsis of a lengthy text, such as a book or article.

Speech recognition: NLP is used in voice recognition, translating spoken language into text so that computers can comprehend and process spoken language.

How Does Google Understand Human Language?

What is the process of Natural Language Processing?

Natural language processing has many applications. However, what is the fundamental mechanism by which natural language is processed?

Generally speaking, there are three primary features of NLP:

Voice recognition: Converting spoken words into text that is readable by machines.

Comprehending natural language: The ability of a computer to understand human speech.

Creating natural language: A computer system's unique production of natural language.

Combining text analysis methods with syntactic and semantic analysis allows computers to comprehend spoken words more deeply. Semantic analysis defines a sentence's meaning, while syntactic analysis focuses on a sentence's grammatical structure.

Using syntactic analysis, natural language is analyzed to determine its compliance with formal grammar principles. Words are evaluated in groups and about one another rather than individually.

Understanding, interpreting, and determining the meaning of words and sentence patterns are all covered by a semantic analysis. This is how a computer processes natural language.

It should be noted that even for humans, which took thousands of years to establish diverse language systems, this remains one of the most challenging parts of machine learning.

An example of a regular exchange between a person and an NLP system:

  •      A human converses with the robot

  •      A machine logs an auditory signal

  •      Text is created from the audio stream by decoding it syntactically and semantically so that it may be examined.

  •      Assessment of reactions and potential measures

  •      converting data into signals, such as text or audio

  •      Language-based text output communication between a machine and a person

  • What does NLP have to do with SEO or Digital Marketing?

What does NLP have to do with SEO or Digital Marketing? 

There is a famous SEO by the name of Kyle Roof, who did not believe that Google and other search engines couldn't read e.g. articles).  He set out to prove this by entering a contest for SEO. Most of us in SEO and digital marketing would use English, right? Not Kyle: He instead uses lorem ipsum (Latin gibberish) and to everyone's amazement was able to rank on Google. He had 30 days to for this contest, and he ended up finishing 5th place for this contest. The story does not end there. In the following weeks, he was able to first place on Google. Kyle got a lot of hate emails telling him he was just lucky. 

Kyle Roof has proved that Google can't read. But rather uses math to rank sites and not content. Now quality content is very important to converting traffic to sales. When you do SEO, you need a tool like Surfer SEO or Page Optimizer Pro to optimize your content so it will rank better on Google. Check out my article on how to make money with affiliate marketing and SEO

Pro tip: Kyle Roof stated that NLP is not a ranking factor. Entity keywords are a ranking factor.  He discovered this when he was doing signal variable testing.  


The ability of computers to understand human language is a remarkable feat made possible through advancements in algorithms, computational linguistics, and machine learning. Through these technologies, computers have been able to process and interpret the complexities of human language, allowing for improved human-computer interaction, natural language processing, and advancements in artificial intelligence. However, it is essential to acknowledge that there are still challenges to overcome, such as language's context sensitivity and intricacies. Nonetheless, progress has paved the way for exciting developments in the field, opening up possibilities for improved communication, information retrieval, and collaboration between humans and machines. The future holds great potential for further advancements in how computers understand human language, ultimately leading to more intelligent and intuitive technologies to serve our needs better.

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