Advanced Neural Networks & NLP Exercise 9
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Questions and Answers

What does the CBOW model in Word2Vec stand for?

  • Continuous Bag of Words (correct)
  • Conditional Basis of Words
  • Combined Bag of Words
  • Common Basis of Words

    • What is the purpose of Named Entity Recognition (NER) in Natural Language Processing?

  • To analyze grammatical structures of sentences
  • To classify text into predefined categories
  • To generate textual summaries from documents
  • To identify and categorize entities within a text (correct)

    • Which statement correctly describes the Skip-Gram model in Word2Vec?

  • It predicts a word given its context.
  • It categorizes words into predefined groups.
  • It predicts context given a word. (correct)
  • It enhances the quality of word embeddings through clustering.

    • In the context of LSTM, which of the following does the previous hidden state represent?

    <p>The output of the previous cell (A)</p> Signup and view all the answers

    What is the primary advantage of Word2Vec in learning word vectors?

    <p>It creates vectors where similar words have similar embeddings. (C)</p> Signup and view all the answers

    What is the primary function of the pooling layer in a Convolution Neural Network?

    <p>To downsample features from the convolution layer (A)</p> Signup and view all the answers

    Which of the following hyperparameters is not associated with the convolutional layer?

    <p>Learning rate (B)</p> Signup and view all the answers

    In Long Short Term Memory networks, what allows the network to selectively remember or forget information?

    <p>Cell states (B)</p> Signup and view all the answers

    What differentiates a fully connected layer from convolutional and pooling layers?

    <p>It connects every input to every neuron (B)</p> Signup and view all the answers

    Which type of pooling operation selects the maximum value of features?

    <p>Max pooling (A)</p> Signup and view all the answers

    What describes the connection patterns of convolution layers?

    <p>They use sparsely connected layers (A)</p> Signup and view all the answers

    What aspect of LSTMs allows for modeling sequences over time?

    <p>Dynamic cell states (C)</p> Signup and view all the answers

    Which filter sizes are commonly used in convolutional layers?

    <p>2x2, 3x3, 4x4, and 5x5 (A)</p> Signup and view all the answers

    Study Notes

    Advanced Neural Networks & NLP

    • Exercise 9 is mentioned.

    Agenda

    • Convolution Neural Networks are discussed
      • Convolutional layer
      • Pooling layer
      • Fully connected layer
    • Text Classification using CNN
    • Long Short Term Memory (LSTM)
    • Word2Vec Embedding
    • Named Entity Recognition

    Convolution Neural Networks

    • Convolutions do not use fully connected layers, instead using sparsely connected ones, accepting matrices as input.
    • Unlike humans who take snapshots, CNNs process images differently.
    • The layers in CNNs are convolutional layer, pooling layer, and fully connected layer.

    Convolutional Layer

    • The convolutional layer uses filters for convolution operations, scanning input images.
    • Hyperparameters include filter size (e.g., 2x2, 3x3, 4x4, 5x5) and stride.
    • The output, called the feature map or activation map, contains features computed from input layers and filters.

    Pooling Layer

    • The pooling layer (POOL) downsamples features, typically after a convolutional layer.
    • Two pooling operations are max pooling and average pooling.
    • Max pooling takes the maximum value of features, and average pooling takes the average.

    Fully Connected Layer

    • The fully connected layer (FC) operates on a flattened input.
    • Each input is connected to all neurons.
    • FC layers are usually at the end of a network, connecting hidden layers to the output layer.
    • FC layers help optimize class scores.

    Text Classification using CNN

    • The input is a sentence matrix.
    • Convolutional feature maps are extracted.
    • Polling representation is calculated.
    • Softmax layer outputs the final classification.

    Long Short Term Memory (LSTM)

    • LSTMs make small modifications to information through multiplications and additions.
    • Information flows through cell states.
    • LSTMs can selectively remember or forget things.
    • Cell state dependencies:
      • Previous cell state (information from the previous time step).
      • Previous hidden state (output of the previous cell).
      • Input at the current time step (new input.)

    Word2Vec Embedding

    • Word2Vec was developed by Tomas Mikolov, et al. and uses neural networks to learn word embeddings.
    • Word2Vec learns by understanding context.
    • The result are vectors for similar words, grouped together.
    • Word2Vec includes two models, CBOW (Continuous Bag of Words) and Skip-Gram.
    • CBOW predicts a word given its context, while Skip-Gram predicts context given a word.

    Named Entity Recognition (NER)

    • In natural language processing, NER is a method for identifying and classifying named entities in text.
    • Text is parsed to categorize entities like names, organizations, locations, quantities, etc.

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    Advanced Neural Networks & NLP PDF

    Description

    This quiz covers advanced concepts in neural networks and natural language processing, including Convolutional Neural Networks (CNNs), LSTMs, and Word2Vec embeddings. Exercise 9 will test your understanding of CNN architecture, layers, and their applications in text classification and named entity recognition.

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