Neural Word Embeddings Quiz

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Questions and Answers

In the context of neural word embeddings, what was the main idea behind word2vec?

To randomly assign vectors to words in a text corpus.

To analyze word relationships using traditional statistical methods.

To compute probabilities of context words for a central word and adjust vectors to increase these probabilities. (correct)

To use a fixed window to determine context words for each central word.

What is the term that has become almost synonymous with 'embeddings' or 'neural embeddings'?

Sliding Window

Neural Network

Central Word

Word2Vec (correct)

How did Mikolov et.al contribute to the idea of neural word embeddings?

Proposed the use of traditional statistical methods for word relationships.

Introduced the term 'neural word embeddings'.

Expanded and improved the idea in their 2013 paper 'word2vec'. (correct)

Presented a Neural Probabilistic Language Model in 2003.

What is the iterative method used in Word2Vec for learning word embeddings?

Computing probabilities of context words and adjusting vectors. Signup and view all the answers

Which paper first presented the idea of Neural Probabilistic Language Model?

'A Neural Probabilistic Language Model' by Bengio et.al Signup and view all the answers

What is the primary task in building a Neural Network for Word2Vec according to the text?

Predict a nearby word given a central word. Signup and view all the answers

What is the primary purpose of the Output layer in word2vec?

To calculate the loss for better word embeddings Signup and view all the answers

Which of the following statements about the training process of word2vec is true?

The training process involves adjusting the matrix values with gradient descent Signup and view all the answers

What is the role of the context window size in word2vec?

It determines the range of words to consider for each target word Signup and view all the answers

What happens to the Output layer after the training process in word2vec?

It is discarded Signup and view all the answers

What is the purpose of the dot product and sigmoid function in word2vec?

To determine the similarity between the target word and context vectors Signup and view all the answers

What is the typical range for the dimensionality of word embeddings in word2vec?

Between 100 and 1,000 Signup and view all the answers

What is the primary purpose of the hidden layer in word2vec?

To convert one-hot representations of words into dense vectors Signup and view all the answers

How does the Continuous Bag of Words (CBOW) model try to predict a target word?

By using the sum of vector embeddings of surrounding words Signup and view all the answers

Why does the Skip-gram model work better in practice than the CBOW model?

It scales better with larger datasets Signup and view all the answers

What is the role of the input layer in word2vec?

To convert input data into one-hot representations Signup and view all the answers

How is the vocabulary of words built in word2vec?

By extracting all unique words from the training documents Signup and view all the answers

What type of neural network is used in word2vec?

Feedforward Neural Network (FNN) Signup and view all the answers

Study Notes

Neural Word Embeddings

Neural word embeddings, also known as word embeddings, are a way to learn relationships between words using neural networks.
The concept was first introduced by Bengio et al. in their 2003 paper "A Neural Probabilistic Language Model".
Mikolov et al. further expanded and improved the idea in their 2013 paper "word2vec", which has become synonymous with neural embeddings.

Word2Vec

Word2Vec is an iterative method that takes a huge text corpus and moves a sliding window over the text to learn word relationships.
The method computes probabilities of context words for a central word and adjusts vectors to increase these probabilities.
Word2Vec learns word embeddings that know viable surrounding words.

Word2Vec Architecture

Step 1: The input layer converts words to one-hot representations to feed into the neural network.
Step 2: The hidden layer has a number of neurons equal to the desired dimension of the word representations.
Step 3: The output layer is a softmax layer with 10,000 neurons, used to calculate the loss and generate better word embeddings.

Word2Vec Training

Training involves sliding a window of size 2C+1 over the training corpus, shifting it by one word each time.
For each target word, the dot product of its vector and context vector is computed and passed through a sigmoid function.
Positive examples have an expected output of 1, while negative examples have an expected output close to 0.
The process is repeated multiple times to train the model.

Word2Vec Results

Hyper-parameter settings include word embedding dimensionality (between 100 and 1,000) and context window size.
There are two models: Continuous Bag of Words (CBOW) and Skip-gram.
CBOW predicts a target word given a context of words, while Skip-gram predicts surrounding words given a target word.

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Description

Test your knowledge about neural word embeddings and their development through papers like 'A Neural Probabilistic Language Model' by Bengio et.al and 'word2vec' by Mikolov et.al. Learn about how neural networks can be used to understand the relationships among words.