Natural Language Processing 101: What It Is and How It Works

August 9, 2023

I used Grammarly to help me write this piece. Grammarly used natural language processing to help me make this article look great.

That’s how prevalent natural language processing use cases have become. NLP technologies have trekked a long way, from writing an article and transcribing sales calls to retrieving large amounts of relevant information and truly understanding what the user means.

The evolution of computational linguistics has made it easy for machines to understand human languages, reducing the gaps between human-computer interactions. Natural language processing software enhances customer experience, automates data entries, improves search recommendations, and strengthens security efforts across industries.

If you’ve used GPS navigation to find your way around a new city or yelled across the room at a voice assistant to switch the lights on – congrats, you’ve met an NLP program!

Thanks to natural language processing, computer applications can respond to spoken commands and summarize large amounts of text in real-time to interact with humans meaningfully and expressively.

How does NLP work?

NLP is all around us, even if we don’t necessarily notice it. Virtual assistants, customer service chatbots, transformer models, predictive text – all are made possible with NLP technology that understands and filters our requests. The programs bridge computers and humans to organize business operations, revitalizing productivity through finely tuned interactions.

The techniques of NLP training rely on deep learning and algorithms to interpret and make sense of human language.  

Deep learning models process unstructured data or qualitative data that cannot be analyzed using conventional tools such as voice and text. They transform it into structured data that can fit into databases we are familiar with to provide usable insights.

Natural language processing extracts contextual information by breaking down language into individual words and identifying their relationships. Doing this allows for a more accurate indexing and segmentation process – one that’s based on sentiment and intent.

Before a model can process any text data, it has to preprocess it into a format the machine can comprehend. There are several data processing techniques available.


Tokenization, the first step for converting raw data into a format the machine can grasp, is dividing the text into smaller units known as tokens. The machine easily understands the text once they’re broken down into words or phrases. Since machines only understand numerical data, the tokenized text is represented as numerical tokens for the programs.


Consider the following text entered by a user:

"There is a bank across the bridge."


Text understood by the machine after tokenization:

["There", "is", "a", "bank", "across", "the", "bridge", "."]

Stop word removal

The next preprocessing step in NLP removes common words with little-to-no specific meaning in the text. These words, known as stop words, include articles (the/a/an), “is,” “and,” are,” and so forth. This step eliminates non-useful words and provides a meaningful, efficient, and accurate understanding of the text.


Consider the exact sample text entered by a user:

"There is a bank across the bridge."

Text understood by the machine after removing stop words:
["There", "bank", "across", "bridge", "."]

Stemming and lemmatization

Stemming and lemmatization refers to the techniques NLP applications use to simplify words and text analysis by reducing them to their base form.

Stemming is a rule-based approach that removes prefixes and suffixes to return the words to their fundamental forms or stems. The process doesn’t require a lot of computational power, and the resulting base words may not always make sense, but they help the program facilitate text analysis.

For example, the word “sharing” will result in a “shar” stem.

A limitation of stemming is that several semantically unrelated words can shareholder share one stem.

Lemmatization is a dictionary-based approach to converting words to their morphological form, aka lemma. The process requires high computational effort due to the need for dictionary lookups. The resulting lemma will always be a valid word contextually and as a part of speech.

For example, the word “sharing” will result in a “share” lemma.

Feature extraction

Since our machine friends only get numbers and algorithms, the raw text we enter must be converted into numerical representations. Feature extraction helps retain the relevant information and simultaneously reduces the complexity of the data to capture only the most necessary patterns and relationships.

Different techniques may be used to achieve this outcome based on the NLP task.

  • Bag-of-Words only considers the presence or absence of words creating a vector space of the text. Representation of the text is done through word frequency rather than word order.
  • Term Frequency-Inverse Document Frequency (TF-IDF) takes into account the importance of every word in the dataset. Frequently occurring words are given more value.
  • Word embeddings capture semantic relationships between words, creating a dense vector representation. Examples include Word2Vec and GloVe.
  • Topic modeling extracts similar topics from text to represent topic-distributed documents. An example of this technique includes Latent Dirichlet Allocation (LDA).

NLP algorithms are generally rule-based or trained on machine learning models. Continuous training and feedback loops can create large knowledge reservoirs, better predict human intention, and minimize false responses.

What are common NLP tasks?

Natural language processing uses AI techniques or tasks to process, comprehend, and generate natural (human) language. They improve human-computer interaction and facilitate effective communication through language-based applications.

Part-of-speech tagging

You know who hasn’t forgotten their 6th-grade grammar lessons? NLP.

Part of speech (POS) tagging, or grammatical tagging, allows NLP applications to identify individual words in a sentence to determine their meaning in the context of that sentence. This allows computers to tell the difference between nouns, verbs, adjectives, and adverbs and understand their relationships.

As shown in the example below, POS tagging means NLP programs have the power to contextualize the verb “like” in the phrase “I like the beach” and identify “like” as an adverb in the sentence “I am like Mark.”

POS Tagging

Word sense disambiguation

The concept isn’t as complicated as it sounds; it just means that NLP programs can identify the intended meaning of the same word when used in different contexts.

Through semantic analysis (i.e., extracting meaning from text and parsing) computers can interpret sentences and relationships between individual words to make the most sense in a particular context.

word sense disambiguation

The word "bark" in the above example has two different meanings.
NLP applications distinguish between a dog’s bark and tree bark through word sense disambiguation.

Named-entity recognition

Natural language processing applications can identify words for specific categories, such as people’s names, places, and names of organizations. Through named-entity recognition (NER), NLP software extracts entities and understands their relationship to the rest of the text.

named entity recognition

In the above example, the NLP task of named entity recognition identifies “Microsoft” and “Bill Gates” as an organization and person, respectively.

Applications of named-entity recognition

  • Extracting facts from fake news: NER can identify important entities that can help verify news sources.
  • Information retrieval: To assist in creating retrieval systems where users can search for specific information to access relevant documents.

Co-reference resolution

High-level NLP tasks such as question answering and information retrieval (more on that later) require computers to identify all words that refer to the same entity. This process,  known as co-reference resolution, helps programs determine persons/objects connected to specific pronouns.

Co-reference resolution is also why computers know when an idiomatic expression is part of a text.

Speech recognition

NLP programs benefit from understanding the process of converting spoken language into – more or less – computer language. Speech recognition is essential for facilitating natural and intuitive human-computer interactions.

Let’s look at a couple of examples of speech recognition as a part of natural language processing.

    • Voice assistants: Our virtual besties Siri, Alexa, and Google Assistant respond to our commands using speech recognition techniques to provide relevant responses.
    • Transcription and dictation: Audio recording transcripts and spoken language-to-text conversions are fundamental for the content creation, legal, and education sectors.
    • Data preprocessing: Speech recognition is important in transforming raw data into a more understandable form. Preprocessing can be done for audio data and textual data.

Information retrieval

NLP programs will always find that important document just when you need it because of their powerful ability to retrieve information from large data sets. The goal of information retrieval as an NLP task is to offer users accurate and useful information from text collection through text mining.

Information retrieval process

Sentiment analysis

Ever wondered how customer service bots can almost always tell how you’re feeling? It’s all thanks to sentiment analysis – an automated process recognizing emotional tone and expressed sentiments in various use cases.

Machine learning models can be trained on sentiment analysis using sentiment labeling (positive, negative, neutral) classification, post-processing, and sentiment evaluation.

Sentiment analysis is a great way for companies to gain customer insight through product reviews and monitor their brands based on social media sentiments.

Machine translation

The NLP task of automatically translating text or spoken content from one language to another is heavily used in machine translation software. Machine translation aims to provide accurate and coherent translations while maintaining contextual precision.

Translation models also use speech recognition. They’re built to improve global communication and break down language barriers in business, education, healthcare, and international relations.

Spam detection

Ever thought an email was legit and replied to it, but it was just spam? Me, too.

The NLP task of automatically recognizing irrelevant messages from a large messaging group, such as emails and social media posts, and removing them is called spam detection.

The process helps distinguish fraudulent messages from genuine ones and ensures the safety of users on communication platforms.

NLP libraries and frameworks

Programming languages are to NLP what a moth is to a flame. Although many languages and libraries support natural language processing tasks, a few popular ones exist.


The most used programming language for NLP tasks, libraries, and deep learning frameworks is written for Python.

  • Natural Language Toolkit (NLTK): One of the first ever NLP libraries written in Python, the NLTK is known for its easy-to-use interfaces and text-processing libraries for tagging, stemming, and semantic analysis. 
  • spaCy: An open-source NLP library, spaCy provides pre-trained vectors. You can use it for NER, part-of-speech tagging, classification, and morphological analysis.
  • Deep learning libraries: PyTorch and TensorFlow are common tools for developing NLP data models.


Statisticians widely use the programming language for statistical computing and graphics NLP models written in R. This includes Word2Vec and TidyText.

Business applications of NLP

Natural language processing techniques are used in many business cases to improve operational efficiency, productivity, and mission-critical processes.

Chatbots and virtual assistants

The rise of conversational AI has transformed how chatbots and virtual assistants engage with humans, especially in customer service.

NLP fuels the human-like capabilities of chatbots to scale automated customer support while maintaining economical operations. Chat and voice bots can offer personalized recommendations and localized chat functionalities to aid in the buying process, answer FAQs, and assist users in real-time.

Speech-to-text features are also beneficial in tracking call center analytics to transcribe voice data into text.


of all users have had at least one conversation with a chatbot.

Source: Tidio

Social media monitoring
Sentiment analysis on social platforms helps evaluate customer feedback and reviews to understand consumer satisfaction through valuable data insights.

Social media monitoring tools are powered by natural language processing to grant listening, tracking, and content collection functionalities. These applications see wide use in performing market research, tracking trend analysis, and identifying patterns across different social networks.

Insights extraction and fraud detection
The healthcare and legal industries use NLP technology to extract high-quality, relevant data insights from large volumes of clinical trial data, scientific literature, and legal contracts.

As with spam detection, NLP technology can detect fraudulent activities by perceiving patterns in data. This is especially useful in the financial sector for monitoring transactions.

NLP vs. NLU vs. NLG

While there’s only one differentiating term in natural language processing, natural language understanding, and natural language generation, a few differences exist among the three concepts. 

Natural language processing

NLP is a branch of AI that helps computers understand, interpret, and generate human language. Common NLP tasks include speech recognition, sentiment analysis, and named entity recognition.

NLP is widely used in voice assisting for summarizing large amounts of text and translation services.

Natural language understanding (NLU)

A subset of NLP, NLU software focuses on the comprehension of the text to extract meaning from the data. It combines software logic, linguistics, ML, and AI to make sense of natural language.

Common NLU tasks include:

  • Intent recognition. NLU models are used to identify the intent of different entities for text classification and categorization purposes. For example, creating different sections for a company's news, entertainment, and business.
  • Content analysis. Understanding connections between pieces of content, NLU can conduct an in-depth analysis of entities to highlight complex sentiments and relationships.
  • Cognitive search. NLU analyzes and extracts unstructured data, allowing it to pull relevant information from diverse datasets. This enhances search query results and provides relevant-intent information using predictive analysis.

Top 5 NLU software

1. Amazon Comprehend
2. IBM Watson Natural Language Classifier
3. Azure Translator Speech API
4. Azure Translator Text API
5. Apace cTAKES

*This data was pulled from the G2 Summer Grid Report on July 19, 2023, based on our scoring methodology.

Natural language generation (NLG)

On the other end of NLU is NLG technology, the branch of AI that generates written or spoken text from a dataset. It lets computers give provide feedback to humans in a language that is understandable to us, not machines.

Common NLG tasks include:

  • Data conversion. NLG models convert structured data to texts readable to humans.
  • Customer interactions. These provide natural language-sounding responses, sentiment matching, and personalized customer communications.

Top 5 NLG software

1. Anyword
2. Quill
3. AX Semantics
4. Wordsmith
5. Phrazor by vPhrase

*This data was pulled from the G2 Summer Grid Report on July 19, 2023, based on our scoring methodology.

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Unlocking the mystery of natural language

While NLP might seem like a sorcerer, it isn’t. It combines various powerful computational abilities making it useful in many tasks that make human tasks more efficient.

Whether it’s through chatbot greetings or text summarization, the world of NLP continues to strive to provide valuable insights from large human language datasets. NLP technologies are making our personal and professional lives more engaging, personalized, and interactive while we navigate our new data-centric world.

One of the most popular NLP functionalities is its usage in voice assistants. Learn more about how voice recognition works and the features it offers that enable you to yell commands at it.

This article was originally published in 2019. It has been updated according to new editorial guidelines, with new resources and recent examples.

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Natural Language Processing 101: What It Is and How It Works Natural language processing uses machine learning and AI to help machines to respond to text and voice data. Learn its common tasks and how it works.
Aayushi Sanghavi Aayushi Sanghavi is a Campaign Coordinator at G2 for the Content and SEO teams at G2 and is exploring her interests in project management and process optimization. Previously, she has written for the Customer Service and Tech Verticals space. In her free time, she volunteers at animal shelters, dances, or attempts to learn a new language.