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Continuous Bag of Words

Continuous Bag of Words (CBOW) is a neural network-based model used for learning word embeddings, which are dense vector representations of words that capture their semantic and syntactic properties. CBOW is a part of the Word2Vec family of models, developed by Mikolov et al. at Google in 2013.

The main idea behind CBOW is that the model learns to predict the target word based on the average of the context words’ embeddings. The CBOW model predicts the target word (center word) given a fixed-size window of context words surrounding it. It does this by using a neural network with a single hidden layer to learn the weights that map the context words to the target word.

Here’s a high-level overview of the CBOW model:

  • Input: The input to the model is a fixed-size window of context words, typically represented as one-hot encoded vectors.
  • Embedding layer: This layer maps the one-hot encoded input vectors to their corresponding embeddings, which are dense, low-dimensional vectors.
  • Average: The model computes the average of the context word embeddings to create a single vector representing the combined context.
  • Hidden layer: The averaged embedding vector is passed through a single hidden layer, which performs a non-linear transformation using an activation function, such as tanh or ReLU.
  • Output layer: The output layer is a linear layer that maps the hidden layer’s output to the target word embedding. The model uses softmax activation to output the probability distribution over the entire vocabulary.
  • Training: The model is trained using stochastic gradient descent (SGD) to minimize the difference between the predicted and the actual target word embeddings.

Once the CBOW model is trained, it can generate embeddings for individual words that capture their semantic and syntactic relationships.

How does CBOW differ from skip-gram model?

CBOW is often compared to another Word2Vec model called Skip-Gram, which reverses the prediction task by using the target word to predict its context words.

What are some applications of CBOW in natural language processing?

CBOW is a valuable tool for understanding and representing the meaning of words in natural language processing tasks. Some of its applications include:

  1. Language Translation: Its ability to capture the semantic and syntactic relationships between words makes it useful for language translation tasks.
  2. Text Classification: It’s employed in text classification tasks where understanding the context of words is important for accurate classification.
  3. Information Retrieval: It can generate word embeddings that capture the semantics of words, which helps with information retrieval tasks.
  4. Sentiment Analysis: By learning the representation of context words and predicting the target word, CBOW can be used for sentiment analysis tasks.
  5. Word Embeddings: Great for learning word embeddings, which are essential for various NLP tasks such as named entity recognition, part-of-speech tagging, and more.

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