What Is Natural Language Processing in Artificial Intelligence? 

    Natural Language Processing (NLP) combines the fields of linguistics and computer science to form a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. What does this mean?

    This means giving a machine the ability to read, understand, and derive meaning from human languages. Primarily, it is concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches.

    And it is clear nowadays that AI has a transformative effect on industries! It not only improves decision-making and enhances efficiency, but also drives innovation, and addresses some of society’s most pressing challenges. So, let’s dive in! 

    Defining NLP

    NLP is the development of algorithms and models that enable computers to sound very human-like by understanding, interpreting, and generating human language in a way that is both meaningful and useful. How so?

    Using deep learning techniques, computers respond intelligently to commands and answer queries in an articulate and super-efficient manner.  Moreover, natural language processing (NLP) applications are wide-ranging and can be found in many aspects of our daily lives.

    To name a few, you might know them as renowned virtual assistants like Siri and Alexa, language translation services such as Google Translate, or even email categorization and recommendation systems like Netflix recommendations.

    What is NLP made up of?

    Natural language processing can be divided into two main subsets as seen in the diagram below: Natural Language Understanding, and Natural Language Generation.

    Natural Language Processing Made Up of

    How Does Natural Language Processing Happen?

    The first step in any NLP project is to collect a large dataset of text data. Basically, this can include books, articles, social media posts, customer reviews, and more. The quality and quantity of the data are crucial for training accurate NLP models. The question remains: What are the steps of natural language processing?

    Process of Natural Language Processing

    Step 1: Segmentation

    It is the process of breaking down the entire document into its constituent sentences. You can do this by segmenting it to its punctuation (full stops, commas..).  

    Step 2: Tokenization in Natural Language Processing

    Tokenization in Natural Language Processing

    For the algorithm to understand these sentences, we must explain individual words in a sentence in a process called Tokenization. 

    Basically, it is breaking down text into individual words, phrases, or symbols, often called tokens (e.g., “unhappiness” might be tokenized into “un-” and “happiness”).

    Step 3: Stop Word Analysis

    This stem consists of getting rid of non-essential words that don’t add meaning to the text for example: are, and, the, was, in, etc… 

    Step 4: Stemming

    Explaining to the machine that some words like jump, jumping, and jumps are the same word with added prefixes and suffixes.

    Step 5: Lemmatization in Natural Language Processing

    Identifying the base words for different word tenses, mood, gender, and more. 

    Major NLP Steps

    Step 6: Part-of-Speech Tagging

    Part-of-Speech-Tagging in Natural Language Processing

    Assigning one of the parts of speech to the given word is generally called POS tagging. 

    In simple words, POS tagging is the task of labeling each word in a sentence with its appropriate part of speech.

    Basically, it is assigning grammatical tags (like nouns, verbs, adjectives) to words in a sentence to understand their syntactic roles.

    Step 7: Named Entity Tagging

    Named Entity Tagging in Natural Language Processing

    Introducing the machine to pop culture references, locations, everyday names, movies, famous people, and more. 

    How is an NLP model trained and finalized?

    Feature Extraction in Natural Language Processing:

    Feature Extraction in Natural Language Processing

    NLP models require numerical data to perform calculations. Therefore, text data needs to be transformed into numerical features. 

    Common techniques include TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings like Word2Vec, GloVe, or BERT embeddings. 

    These methods represent words and documents as vectors in a high-dimensional space.

    Model Building:

    1. Rule-Based Models: Simple NLP tasks like keyword matching or regular expressions can be used to extract information or perform basic tasks.
    2. Machine Learning Models: More complex tasks, such as sentiment analysis, text classification, or named entity recognition, often involve supervised machine learning algorithms. In detail, these include the likes of Support Vector Machines, Naive Bayes, or deep learning models like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
    3. Transformer Models: Transformer-based models, like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have revolutionized NLP. Basically, they are pre-trained on vast amounts of text data and can be fine-tuned for specific NLP tasks.

    Training of Natural Language Processing Models: 

    Machine learning models are trained on labeled datasets. In detail, the model learns to make predictions by minimizing a loss function. For example, in the case of deep learning models like Transformers, they are trained on massive datasets and fine-tuned on smaller, task-specific datasets.


    NLP models need to be evaluated to ensure their performance and accuracy. Common evaluation metrics include accuracy, precision, recall, F1-score, and more, depending on the specific task.

    Fine-Tuning and Iteration: 

    NLP models often require fine-tuning and ongoing maintenance to adapt to changing language patterns, new data, or task-specific requirements. This involves retraining or updating the model as needed.

    Deployment of Natural Language Processing Models: 

    Finally, once a model performs satisfactorily, it can be deployed in real-world applications, such as chatbots like Chat-GPT, language translation services, content recommendation systems, and more.

    What is Natural Language Processing used for?


    Natural language processing can be used to make digital content more accessible to people with disabilities. Text-to-speech and speech-to-text technologies, for instance, help individuals with visual or hearing impairments interact with digital content.

    Sentiment Analysis and Opinion Mining: 

    NLP helps businesses and organizations analyze public sentiment and opinions expressed on social media, customer reviews, and other sources. We can use this information for market research, customer feedback, and reputation management.

    Machine Translation:

    NLP plays a vital role in machine translation since it allows AI systems to automatically translate text or speech from one language to another. And this is valuable for breaking down language barriers in a globalized world.

    Speech Recognition in Natural Language Processing:

    Converting spoken language into written text, as in speech-to-text systems. In detail, machine learning and neural networks process audio data and convert it into words that can be used in businesses.

    Text/Language Generation:

    NLP models can generate human-like text, which has applications in content creation, chatbot responses, and even creative writing. 

    GPT-3, for example, is a well-known NLP model capable of generating coherent and contextually relevant text.

    Question Answering:

    Question answering provides cloud-based Natural Language Processing (NLP) that allows you to create a natural conversational layer over your data. 

    In detail, we use it to find appropriate answers from customer input, a project, a given text, or a database in response to natural language questions.

    Text Summarization Using Natural Language Processing:

    Text summarization refers to the technique of shortening long pieces of text. Basically, the intention is to create a coherent and fluent summary with only the main points outlined in the document. 

    Automatic text summarization is a common problem in machine learning and natural language processing (NLP). So, creating concise summaries of longer texts can be useful for quickly understanding the main points of a document.

    Key Challenges Facing NLP

    Key Challenges Natural Language Processing

    • Ambiguity: Words and phrases can have multiple meanings depending on context. 
    • Syntax and Semantics: Understanding the syntax (structure) and semantics (meaning) of language is a complex task. Since different languages have different rules, and even within a single language, there can be variations and nuances.
    • Context: NLP models need to understand and consider context, as the meaning of a word or phrase can change based on what came before it in a sentence or paragraph.
    • Data Availability: NLP models, especially deep learning models, require vast amounts of high-quality training data. Obtaining and preparing such data can be expensive and time-consuming.
    • Bias and Fairness: NLP models often inherit biases from the data they are trained on, which can lead to biased or unfair outcomes, such as gender or racial bias. Mitigating these biases is a significant challenge.
    • Multilingual and Cross-lingual Understanding: Language is not standardized, and people use slang, idioms, abbreviations, and colloquialisms. So, developing NLP models that can work effectively across multiple languages and understand the relationships between languages is a complex task. 
    • Domain Adaptation: NLP models trained in one domain may not perform well in a different domain. Adapting models to specific domains or tasks can be challenging.
    • Privacy and Security: NLP models can inadvertently reveal sensitive information if not properly designed and secured. Ensuring privacy and security in NLP applications is a growing concern.
    • Real-Time Processing: Some NLP applications, such as chatbots or real-time translation, require low-latency processing, which can be challenging for resource-intensive models.
    • Ethical Considerations: Decisions made by NLP models can have ethical implications. Ensuring responsible and ethical use of NLP technology is a  growing concern.

    NLP and Artificial Intelligence

    In essence, NLP is a core component of AI because it enables machines to work with and understand human language, which is a crucial aspect of many AI applications

    NLP techniques and models continue to advance, making AI systems more capable of natural and effective communication with humans, and thus expanding the practical applications of AI in various domains. Want to learn how to make money with AI? Check out our guide here!

    And as always, stay tuned for more!


    Please enter your comment!
    Please enter your name here

    Stay in the Loop

    Stay in the loop with blockchain Witcher and get the lastest updates!


    Latest stories

    You might also like...