If you’re new to the world of Natural Language Processing (NLP) or you’ve heard of BERT but aren’t sure how it fits in, you’re in the right place. In this blog post, we’ll break down the difference between NLP vs BERT, using simple terms and easy-to-follow code examples.
What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that deals with the interaction between computers and human languages. Simply put, NLP helps computers understand, interpret, and generate human language in a useful way.
Some common NLP tasks include:
- Text classification: Categorizing text into predefined categories. For example, spam or not spam.
- Named Entity Recognition (NER): Finding names of people, places, or organizations in a text.
- Machine Translation: Translating text from one language to another.
- Sentiment Analysis: Determining if a text is positive, negative, or neutral.
NLP has been around for a long time and uses various techniques, including rule-based systems, machine learning, and deep learning. But NLP on its own is broad and includes multiple approaches.
What is BERT?
BERT stands for Bidirectional Encoder Representations from Transformers. It’s a state-of-the-art language model developed by Google in 2018. BERT is specifically designed for deep learning NLP tasks. It’s based on a concept called transformers, which helps models understand the context of a word by looking at the words around it (not just left-to-right but also right-to-left).
With BERT, the model can grasp a better understanding of the context behind words, improving performance on a wide range of tasks like question answering or text summarization.
Key Differences Between NLP vs BERT
| Aspect | NLP | BERT |
|---|---|---|
| Definition | A field of AI for understanding human language. | A specific deep learning model for NLP tasks. |
| Techniques | Includes rule-based, machine learning, and deep learning techniques. | Built using the transformer architecture for deep learning. |
| Context Understanding | Varies depending on the method used (not always bidirectional). | Fully bidirectional, allowing for better context understanding. |
| Performance | Good, depending on the technique used. | State-of-the-art performance in many NLP tasks. |
| Training Data | Can use varied datasets. | Pre-trained on a large corpus (Wikipedia, BookCorpus). |
| Common Uses | Sentiment analysis, translation, summarization, etc. | Question answering, text summarization, NER, etc. |
NLP Without BERT – Simple Example
Let’s look at a simple NLP example without using BERT. We’ll do sentiment analysis using a traditional bag-of-words model.
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
# Sample Data
texts = ["I love Linux!", "This is terrible!", "I'm so happy with this laptop!", "The service was horrible!"]
labels = [1, 0, 1, 0] # 1: Positive, 0: Negative
# Create a model
model = make_pipeline(CountVectorizer(), MultinomialNB())
# Train the model
model.fit(texts, labels)
# Test the model
test_text = ["Linux is amazing!"]
print(model.predict(test_text)) # Output: [1] (positive sentiment)This simple model works fine for basic sentiment analysis, but it doesn’t understand the context well. For example, it wouldn’t distinguish between “not good” and “good” because it treats each word separately.
Using BERT for NLP Tasks
Let’s see how using BERT improves NLP tasks by better understanding the context. Below is an example of using BERT for sentiment analysis with the transformers library.
In this example, BERT gives us a POSITIVE sentiment with an impressive score of 0.9876914620399475. It can catch the meaning and context far better than traditional models.
Why Use BERT?
- Better Context Understanding: BERT looks at both sides of a word (left and right), making it great for tasks that require understanding the full context.
- Pre-trained: BERT comes pre-trained on huge datasets, so it understands language nuances better than models trained from scratch.
- Transfer Learning: You can fine-tune BERT on specific NLP tasks, making it more powerful. (Follow this link)
Final Thoughts
NLP is a broad field with many approaches, but BERT is one of the most powerful tools for deep learning tasks. If you’re working with complex NLP projects like question answering or summarization, BERT is your go-to model. Traditional NLP models are still useful for simpler tasks, but BERT gives you that extra edge with its advanced context understanding.
