Word Embeddings in Natural Language Processing (Word2Vec and GloVe)
Traditional text representation techniques like Bag of Words and TF-IDF fail to
capture the meaning and context of words. Word embeddings solve this problem by
representing words as dense numerical vectors that encode semantic relationships.
With word embeddings, words with similar meanings appear closer together in vector
space. This breakthrough made modern NLP systems such as chatbots, translators,
and recommendation engines possible.
⭐ What are Word Embeddings?
Word embeddings are vector representations of words where semantic meaning,
context, and relationships are learned from large text corpora. Unlike sparse vectors,
embeddings are dense and low-dimensional.
📌 Why Word Embeddings are Important
- Capture semantic meaning of words
- Understand context and similarity
- Reduce dimensionality
- Improve NLP model accuracy
📌 Word2Vec
Word2Vec is a neural network-based model developed by Google that learns word
embeddings from large datasets by predicting word relationships.
Word2Vec Architectures:
- CBOW (Continuous Bag of Words): Predicts a word from its context
- Skip-Gram: Predicts surrounding words from a target word
Example Using Word2Vec:
from gensim.models import Word2Vec
sentences = [
["deep", "learning", "is", "powerful"],
["nlp", "uses", "word", "embeddings"]
]
model = Word2Vec(
sentences,
vector_size=100,
window=5,
min_count=1,
workers=4
)
print(model.wv["learning"])
Key Features of Word2Vec:
- Learns word similarity automatically
- Efficient on large datasets
- Supports analogy reasoning
📌 GloVe (Global Vectors for Word Representation)
GloVe is a count-based embedding model that learns word vectors using global
co-occurrence statistics from a corpus. It combines the advantages of matrix
factorization and neural network methods.
How GloVe Works:
- Builds a word co-occurrence matrix
- Uses matrix factorization to learn embeddings
- Preserves global statistical information
Why Use GloVe?
- Captures global context better than Word2Vec
- Pretrained models are widely available
- Performs well on semantic similarity tasks
📌 Comparison: Word2Vec vs GloVe
- Word2Vec: Predictive, neural network-based
- GloVe: Count-based, matrix factorization approach
📌 Real-Life Applications
- Search engines and semantic search
- Chatbots and virtual assistants
- Recommendation systems
- Machine translation
📌 Project Title
Semantic Word Similarity and Analogy System
📌 Project Description
In this project, you will build a semantic similarity system using Word2Vec or
pretrained GloVe embeddings. The system will identify similar words and solve
analogy-based problems such as “king – man + woman = queen”.
📌 Summary
Word embeddings are a major milestone in NLP evolution. By representing words as
dense vectors, models gain the ability to understand meaning and relationships.
Word2Vec and GloVe form the foundation for advanced models like BERT and GPT.
