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Questions and Answers
What is a dense vector in the context of word embeddings?
What is a dense vector in the context of word embeddings?
A dense vector is a numerical representation that captures specific properties of words.
Word embeddings can be averaged to get sentence embeddings.
Word embeddings can be averaged to get sentence embeddings.
True (A)
Which model is mentioned as an example of word embeddings?
Which model is mentioned as an example of word embeddings?
- Elmo
- GloVe
- FastText
- word2vec (correct)
The boy ___ rice -> eats.
The boy ___ rice -> eats.
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What does P(w|c) represent in word2vec?
What does P(w|c) represent in word2vec?
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What is the task performed by the Skip-gram model in word2vec?
What is the task performed by the Skip-gram model in word2vec?
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Neural networks use ___ for learning through examples.
Neural networks use ___ for learning through examples.
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Study Notes
Neural Networks for NLP
- This module covers the use of Neural Networks for Natural Language Processing (NLP)
- It explores shallow networks for learning word representations, sequential networks for learning sentence representations, and parallelized neural networks using attention
- It also covers attention, transformers, and transformer-based models
- The module concludes with a discussion of applications and frontiers in NLP
Word Embeddings
- Word embeddings are dense vectors that represent words
- These vectors capture specific properties of words
- Word embeddings can be used as feature vectors in a statistical classifier
- Example: word2vec
- Word embeddings can be averaged to create sentence embeddings
- This is effective for obtaining structured representations from unstructured text input
Word2vec
- Word2vec is an example of a method for generating word embeddings
- It can be implemented using two models: Continuous Bag-of-Words (CBOW) and Skip-gram
- CBOW aims to predict the center word given its context (other words around it)
- Skip-gram aims to predict the context words given the center word
Neural network representation of word2vec
- The word2vec model can be represented by a neural network
- The network utilizes backpropagation for learning, where weights are adjusted to minimize the error of the predictions
- It predicts the target word probabilities based on the input word embedding
- This allows the network to learn relationships and correlations between words
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