Count-based and prediction-based word embeddings are two different approaches to representing words in a continuous vector space. They differ in how they capture the semantic relationships between words and the methods used for training. Here's a comparison of the two:
Count-Based Word Embeddings:
Approach: Count-based word embeddings are based on the frequency of word co-occurrences in a large corpus of text. They use statistics derived from word co-occurrence counts to construct word vectors.
Word Co-occurrence Matrix: The primary data structure used is the word co-occurrence matrix, where each entry (i, j) represents the number of times word i and word j appear together in the corpus. This matrix is typically very large and sparse.
Dimensionality Reduction: Techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) are applied to the co-occurrence matrix to reduce its dimensionality while preserving the most important information.
Semantic Similarity: Count-based embeddings capture semantic similarity by identifying words that co-occur frequently in similar contexts. Words with similar embeddings are those that tend to appear in similar contexts across the corpus.
Example: Latent Semantic Analysis (LSA) is an example of a count-based word embedding technique.
Prediction-Based Word Embeddings:
Approach: Prediction-based word embeddings, also known as neural word embeddings, are trained using neural network architectures that predict word representations based on the surrounding context words.
Local Context: These models consider local context, often a window of neighboring words, to predict the target word's embedding. They use techniques like Skip-gram and Continuous Bag-of-Words (CBOW) to make these predictions.
End-to-End Training: Prediction-based embeddings are learned through end-to-end training of neural networks, where the objective is to minimize the difference between predicted and actual word representations. This training process adjusts the word embeddings iteratively.
Semantic Similarity: Prediction-based embeddings capture semantic similarity by learning to predict contextually similar words. Words with similar embeddings are those that tend to have similar roles and meanings in sentences.
Examples: Word2Vec (Skip-gram and CBOW models), GloVe (Global Vectors for Word Representation), and embeddings learned by transformer-based models like BERT are examples of prediction-based word embeddings.
Key Differences:
Data Usage: Count-based embeddings rely on global word co-occurrence statistics from the entire corpus, while prediction-based embeddings use local context information to predict word embeddings.
Training Method: Count-based embeddings often involve matrix factorization techniques, while prediction-based embeddings use neural network-based models.
Sparsity: Count-based embeddings can be sparse, especially if the vocabulary is large, while prediction-based embeddings are typically dense.
Context Handling: Count-based embeddings consider co-occurrence statistics without explicitly modeling context. Prediction-based embeddings explicitly model context and are better suited for capturing contextual information.
Performance: Prediction-based embeddings have shown superior performance on a wide range of natural language processing tasks due to their ability to capture rich semantic relationships, including word analogies and word sense disambiguation.
In summary, count-based word embeddings rely on co-occurrence statistics and dimensionality reduction techniques, while prediction-based word embeddings use neural networks to predict word representations from local context. Prediction-based embeddings have become more popular in recent years due to their ability to capture contextual semantics effectively.