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

Flashcards

Long Short Term Memory (LSTM)

A type of neural network specifically designed to process sequential data, such as text or time series. It has a 'memory' that allows it to retain relevant information from previous inputs, making it well-suited for tasks like language understanding and translation.

Word2Vec Embedding

A technique for representing words as numerical vectors, where words with similar meaning have similar vector representations. It learns these representations by analyzing the context in which words appear.

Continuous Bag of Words (CBOW)

A model within Word2Vec that predicts a word based on its surrounding words. It learns by trying to guess the target word given its context.

Skip-Gram Model

Another model within Word2Vec, but it works in reverse by predicting surrounding words based on a given target word. It aims to identify the context of a word by learning to predict the words around it.

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Named Entity Recognition (NER)

A task in natural language processing that involves identifying and classifying named entities in text, such as people, organizations, locations, and dates. It helps computers 'understand' the meaning behind words and phrases.

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Convolutional Neural Networks (CNNs)

A type of neural network that uses sparsely connected layers (matrices) as inputs instead of fully connected layers like traditional networks. This allows CNNs to process images efficiently by focusing on specific features.

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Convolutional Layer

A layer in CNNs that applies filters (small matrices) to the input image, scanning it and extracting features. The size of the filter and the stride (movement step) determine how the filter moves across the image.

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Pooling Layer (Max or Average)

A layer in CNNs that reduces the dimensionality of the feature maps from the convolutional layers. This is done by summarizing features within a region of the image, such as taking the maximum or average value. This helps reduce computational load and prevent overfitting.

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Fully Connected Layer (FC)

A layer in CNNs that takes the flattened output from the convolutional and pooling layers and connects it to the output layer. This allows the network to classify the image based on the learned features.

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Named Entity Recognition

A task in Natural Language Processing (NLP) that aims to identify named entities, such as persons, organizations, and locations, within text. It helps extract structured information from text.

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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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