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Q1. Explain Word2Vec Algorithm.

Ans: Word2Vec is a popular natural language processing (NLP) technique used for generating word embeddings, which are vector representations of words. The model was introduced by Tomas Mikolov and his colleagues at Google in 2013. Word2Vec aims to capture the semantic meaning of words by representing them as continuous vector spaces. Here's a simplified explanation of how Word2Vec works:

1. Context and Target Words:

  • Word2Vec operates on the principle that the meaning of a word is closely related to the words that surround it in a given context.
  • A context window is defined for each target word. The context consists of words that appear nearby within a certain range.

2. Training Data:

  • The model is trained on a large corpus of text data. This could be a collection of documents, books, articles, or any other text source.

3. One-Hot Encoding:

  • Before training, words are typically represented using one-hot encoding. Each word in the vocabulary is assigned a unique index, and a vector is created where all elements are zero except for the element at the index corresponding to the word, which is set to 1.

4. Neural Network Architecture:

  • Word2Vec employs a neural network with a hidden layer that serves as the embedding layer. There are two main architectures for Word2Vec: Continuous Bag of Words (CBOW) and Skip-gram. Here, we'll focus on the Skip-gram model.

5. Skip-gram Architecture:

  • Given a target word, the model tries to predict the context words that are likely to appear around it.
  • The input to the model is the one-hot encoded vector of the target word, and the output is the probability distribution of context words.

6. Training Objective:

  • The model is trained to maximize the likelihood of predicting the correct context words for a given target word.
  • This involves adjusting the weights of the neural network to minimize the difference between the predicted probability distribution and the actual distribution of context words.

7. Word Embeddings:

  • Once the model is trained, the weights of the hidden layer (embedding layer) for each word are used as the word embeddings.
  • These embeddings are dense vectors that capture semantic relationships between words. Words with similar meanings are closer together in the vector space.

8. Similarity and Relationships:

  • The learned embeddings can be used to calculate similarity between words. Similar words will have similar vector representations.
  • Vector operations, such as vector addition and subtraction, can be used to explore relationships between words (e.g., "king" - "man" + "woman" results in a vector close to "queen").

9. Applications:

  • The trained Word2Vec model can be used for various NLP tasks such as text classification, sentiment analysis, and machine translation, as well as for exploring semantic relationships between words.

In summary, Word2Vec is a powerful technique for generating word embeddings by training neural networks to predict context words for a given target word. The resulting dense vector representations capture semantic relationships between words, enabling the model to learn meaningful representations of words in a continuous vector space.

Q 1.a) Explain Skip-gram model architecture in more detail.

Ans: Certainly! Here's a revised and accurate explanation of the Skip-gram model architecture:

Skip-gram Model Architecture:

  1. Context and Target Words:

    • Given a target word in a sentence, the Skip-gram model aims to predict the surrounding context words.

  2. Input Representation:

    • The target word is represented using one-hot encoding, creating a vector where all elements are zero except for the index corresponding to the target word, which is set to 1.

  3. Embedding Layer:

    • The one-hot encoded vector is multiplied by a weight matrix, creating an embedding vector directly without applying an activation function.

  4. Hidden Layer:

    • There is no activation function applied directly to the hidden layer in the original Skip-gram model.
    • The output of the embedding layer serves as the hidden layer representation.

  5. Output Layer:

    • The output layer is a softmax layer that produces a probability distribution over the vocabulary.
    • Each element in the output vector corresponds to the probability of a specific context word occurring given the target word.

  6. Training Objective:

    • The model is trained to maximize the likelihood of predicting the correct context words for a given target word.
    • This involves adjusting the weights to minimize the difference between the predicted probability distribution and the actual distribution of context words.

  7. Loss Function:

    • The negative log likelihood is commonly used as the loss function to penalize incorrect predictions.

  8. Backpropagation:

    • Backpropagation is employed to adjust the weights in the embedding layer, optimizing the model for better performance.

  9. Word Embeddings:

    • Once trained, the weights of the embedding layer are used as the word embeddings.
    • These embeddings are dense vectors that capture semantic relationships between words.

  10. Applications:

    • The learned embeddings can be applied to various natural language processing tasks, such as text classification, sentiment analysis, and machine translation.

In summary, the Skip-gram model is a neural network architecture designed to learn distributed representations of words by predicting context words given a target word. The resulting word embeddings capture semantic relationships and are valuable for a wide range of NLP applications.

Q 1.b) Explain CBOW Model Architecture.

Ans: Certainly! The Continuous Bag of Words (CBOW) model is the other architecture used in Word2Vec for learning word embeddings. Unlike Skip-gram, CBOW aims to predict the target word given its surrounding context words. Here's a step-by-step explanation of the CBOW model architecture:

CBOW Model Architecture:

  1. Context Words and Target Word:

    • Given a set of context words (surrounding words in a sentence), CBOW aims to predict the target word.

  2. Input Representation:

    • The context words are represented using one-hot encoding and averaged or summed to create an input vector.

  3. Embedding Layer:

    • The averaged or summed vector is multiplied by a weight matrix, creating an embedding vector.

  4. Hidden Layer:

    • There is no activation function applied directly to the hidden layer in the original CBOW model.
    • The output of the embedding layer serves as the hidden layer representation.

  5. Output Layer:

    • The output layer is a softmax layer that produces a probability distribution over the vocabulary.
    • Each element in the output vector corresponds to the probability of a specific target word occurring given the context.

  6. Training Objective:

    • The model is trained to maximize the likelihood of predicting the correct target word given the context.
    • This involves adjusting the weights to minimize the difference between the predicted probability distribution and the actual distribution of target words.

  7. Loss Function:

    • The negative log likelihood is commonly used as the loss function to penalize incorrect predictions.

  8. Backpropagation:

    • Backpropagation is employed to adjust the weights in the embedding layer, optimizing the model for better performance.

  9. Word Embeddings:

    • Once trained, the weights of the embedding layer are used as the word embeddings.
    • These embeddings are dense vectors that capture semantic relationships between words.

  10. Applications:

    • The learned embeddings can be applied to various natural language processing tasks, such as text classification, sentiment analysis, and machine translation.

In summary, the CBOW model is a neural network architecture designed to learn distributed representations of words by predicting the target word given its context. The resulting word embeddings capture semantic relationships and are valuable for a wide range of NLP applications.