Review:
Word Embeddings (word2vec, Glove)
overall review score: 4.5
⭐⭐⭐⭐⭐
score is between 0 and 5
Word embeddings such as Word2Vec and GloVe are techniques used in natural language processing to map words into continuous vector spaces. These embeddings capture semantic and syntactic relationships between words, enabling algorithms to understand contextual similarities and differences. They are foundational tools in various NLP applications, including machine translation, sentiment analysis, and information retrieval.
Key Features
- Capture semantic relationships between words through vector proximity
- Learn dense, low-dimensional representations of words from large corpora
- Enable analogical reasoning (e.g., king - man + woman ≈ queen)
- Pre-trained embeddings available for quick deployment
- Efficient training techniques suited for large datasets
Pros
- Effectively encode complex semantic and syntactic relationships between words
- Improve performance of many NLP tasks when integrated into models
- Pre-trained models save time on training from scratch
- Relatively computationally efficient compared to earlier methods
- Widely accepted and supported in the NLP community
Cons
- Limited to capturing only linear relationships and may miss nuanced context
- Static embeddings do not account for word meanings changing with context (e.g., polysemy)
- Require large amounts of data for meaningful training
- May contain biases present in training corpora
- Not as expressive as newer contextual models like transformers (e.g., BERT)