The Continuous Bag of Words (CBOW) is a model used in natural language processing. It’s designed to predict a target word based on its context. Here’s a more detailed explanation:
Concept:
- Goal: CBOW aims to predict a word given the context in which it appears. The context typically consists of a window of surrounding words.
- “Bag of Words”: The term “bag of words” suggests that the order of words in the context is not strictly followed; it’s more about the presence of certain words around the target word.
How CBOW Works:
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Input Layer: The input to the model is the set of context words. Each of these words is represented as one-hot encoded vectors (binary vectors with a ‘1’ in the position of the word in the vocabulary and ‘0’s elsewhere).
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Projection Layer: These vectors are then projected onto a shared hidden layer, but instead of using a traditional neural network weight matrix to do so, the projection is the average of these vectors.
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Output Layer: The hidden layer then connects to the output layer. The output layer is a softmax layer that outputs a probability distribution over the entire vocabulary. The word with the highest probability is the model’s prediction for the target word.
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Learning: During training, the weights between the hidden layer and the output layer are adjusted through backpropagation. The objective is to maximize the probability of the correct target word.
Example:
Consider the sentence: “The cat sat on the [target word]”. In a CBOW model, ‘The’, ‘cat’, ‘sat’, ‘on’, ‘the’ are context words and are used as input to predict the missing target word.
Advantages and Uses:
- Efficiency: CBOW is faster to train than some other models (like the Skip-Gram model of Word2Vec) because it predicts a single word based on the context rather than multiple context words based on a single word.
- Handling Noise: It’s generally better at handling noise in the data.
- Common Usage: It’s widely used for tasks like word embedding generation, where the semantic relationship between words is captured.
Limitations:
- Context Limitation: It only considers a fixed window of context words and might miss broader contexts.
- Word Order: It doesn’t consider the order of words, which can be crucial in understanding the meaning of sentences.
In summary, CBOW is a method in word embedding that predicts a target word based on its context words, used for efficiently capturing semantic relationships in a corpus of text.