GenAI is Not Equal to NLP: Understanding the Key Differences

Introduction

In the rapidly evolving world of artificial intelligence (AI), terms like Generative AI (GenAI) and Natural Language Processing (NLP) are often used interchangeably, leading to confusion. While both fields are closely related and often overlap, they are not the same thing. Understanding the distinctions between them is crucial for businesses, developers, and AI enthusiasts looking to leverage these technologies effectively.

In this article, we’ll break down:

  • What NLP is and its primary applications
  • What GenAI is and how it differs from NLP
  • Where the two fields intersect
  • Why the distinction matters

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a subfield of AI focused on enabling computers to understand, interpret, and manipulate human language. It involves tasks such as:

  • Text classification (e.g., spam detection, sentiment analysis)
  • Named Entity Recognition (NER) (identifying names, dates, locations in text)
  • Machine Translation (e.g., Google Translate)
  • Speech Recognition (e.g., Siri, Alexa)
  • Question Answering (e.g., chatbots, search engines)

NLP relies heavily on linguistic rules, statistical models, and machine learning to process structured and unstructured language data. Traditional NLP systems were rule-based, but modern NLP leverages deep learning (e.g., Transformer models like BERT, GPT) for more advanced capabilities.

What is Generative AI (GenAI)?

Generative AI (GenAI) refers to AI models that can generate new content, such as text, images, music, or even code. Unlike NLP, which primarily focuses on understanding and processing language, GenAI is about creating original outputs.

Key examples of GenAI include:

  • Text Generation (e.g., ChatGPT, Claude, Gemini)
  • Image Generation (e.g., DALL·E, Midjourney, Stable Diffusion)
  • Code Generation (e.g., GitHub Copilot)
  • Audio & Video Synthesis (e.g., AI voice clones, deepfake videos)

GenAI models are typically built on large language models (LLMs) or diffusion models (for images/videos) and are trained on massive datasets to produce human-like outputs.

Key Differences Between NLP and GenAI

FeatureNLPGenAI
Primary GoalUnderstand & process languageGenerate new content
ApplicationsTranslation, sentiment analysisText/image/code generation
OutputStructured analysis (e.g., labels)Creative content (e.g., essays, art)
Models UsedBERT, spaCy, NLTKGPT-4, DALL·E, Stable Diffusion
FocusAccuracy in language tasksCreativity & novelty in outputs

Where Do NLP and GenAI Overlap?

While they serve different purposes, NLP and GenAI often intersect:

  1. LLMs (Like GPT-4): These models are trained using NLP techniques but are used for generative tasks.
  2. Chatbots: Some use NLP for understanding queries and GenAI for generating responses.
  3. Summarization: NLP extracts key information; GenAI rewrites it in a new form.

However, not all NLP is generative, and not all GenAI is language-based (e.g., image generators).

Why Does This Distinction Matter?

  1. Choosing the Right Tool
    • Need text analysis? Use NLP models like BERT.
    • Need creative writing? Use GenAI like ChatGPT.
  2. Ethical & Business Implications
    • NLP biases affect decision-making.
    • GenAI raises concerns about misinformation, copyright, and deepfakes.
  3. Technical Implementation
    • NLP pipelines focus on data preprocessing, tokenization, and classification.
    • GenAI requires prompt engineering, fine-tuning for creativity, and safety checks.

Conclusion

While NLP and GenAI are related, they serve fundamentally different purposes:

  • NLP = Understanding language.
  • GenAI = Creating new content.

As AI continues to evolve, recognizing these differences will help businesses, developers, and policymakers deploy the right solutions for their needs.

Introduction to Sentiment Analysis using Stanford NLP

Nowadays consumer forums, surveys, social media are generating huge text content like never before compared to the last decade.

Interesting use-cases can be brand monitoring using social media data, voice of customer analysis etc.

Thanks to research in Natural Language Processing (NLP), many algorithms, libraries have been written in programming languages such as Python for companies to discover new insights about their products and services.

Popular NLP Libraries in Python

NLTK (Natural Language Toolkit) is a huge corpus of human language data built as open-source package for Python language. It performs tasks such as tokenization, parsing, classification, tagging, semantic reasoning.

There are other prominent libraries in place. For instance, Textblob is built on top of NLTK package. And, there are other libraries such as Spacy, Gensim, Stanford Core NLP.

Common NLP Tasks

In a nutshell, there are many text related tasks we can think of such as tokenization, parts of speech (pos) tagger, named entity recognition, coreference resolution, sentiment analysis, stemming, lemmatization, stopwords removal, singularize/pluralize, ngram, spellcheck, summarizing text, topic modeling and the kind of linguistic languages we’re dealing with.

In this article, I’d like to share a simple, quick way to perform sentiment analysis using Stanford NLP.

The outcome of a sentence can be positive, negative and neutral. In general sense, this is derived based on two measures: a) Polarity and b) Subjectivity.

Polarity score ranges between -1 and 1, indicating sentiment as negative to neutral to positive whereas Subjectivity ranges between 0 and 1 indicating objective when it is closer to 0 – factual information and subjective when closer to 1.

Stanford NLP is built on Java but have Python wrappers and is a collection of pre-trained models. Let’s dive into few instructions…

  1. As a pre-requisite, download and install Java to run the Stanford CoreNLP Server.
  2. Download Stanford CoreNLP English module at https://stanfordnlp.github.io/CoreNLP/download.html#getting-a-copy
  3. Navigate to its path in your downloaded folder. Unzip the files. Go to your command prompt and type the following command to run its server. Note: -mx4g option is to state 4 gigabytes memory to be used.
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 50000

Launch your Python Jupyter notebook or IDE (Ex: Spyder) and run down this code. Ensure you install StanfordCoreNLP package using PIP install command.

from pycorenlp import StanfordCoreNLP

nlp = StanfordCoreNLP('http://localhost:9000')

text = "The intent behind the movie was great, but it could have been better"
results = nlp.annotate(text,properties={
        'annotators':'sentiment, ner, pos',
        'outputFormat': 'json',
        'timeout': 50000,
        })

for s in results["sentences"]:
    print("{} : {}".format(" ".join(t["word"] for t in s["tokens"]),s["sentiment"]))

Annotate allows us to call specific NLP tasks such as Sentiment analysis. It returns output in JSON format.

Once you run the code, you can terminate the Java server by typing Ctrl + C and hitting enter in the command prompt.

Stanford NLP supports multiple languages other than English. You can follow the documentation provided at https://stanfordnlp.github.io/CoreNLP/

For a sample quick analysis, try this link which connects you to Stanford NLP http://corenlp.run/ instance. Type a sentence and explore the visual representation of some of the analysis.

You can refer the same sample code on my GitHub: https://github.com/coffeewithshiva/Sentiment_Analysis_Stanford_NLP

On Textblob, I came across the below GitHub which might be extensively useful: https://github.com/shubhamjn1/TextBlob/blob/master/Textblob.ipynb

Happy NLP!