Lecture 3 - Structured Inputs
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Questions and Answers

What is a significant limitation of the zero-one and normalized frequency encoding approaches?

  • They limit the vocabulary size significantly.
  • They do not maintain the sequential order of words. (correct)
  • They can only represent numerical data.
  • They require extensive computational resources.
  • What do bigrams represent in text analysis?

  • Pairs of consecutive tokens treated as a single unit. (correct)
  • Single words treated as separate units.
  • Three consecutive words as a standalone vocabulary.
  • Any sequence of tokens greater than three.
  • Which of the following is an example of a static feature that can add value to a text analysis model?

  • Token frequency in an external database.
  • Average length of words within the text. (correct)
  • Use of synonyms to extend vocabulary.
  • Sentiment analysis of the input text.
  • In the context of DNA analysis, what role does DNA serve?

    <p>As a blueprint for the formation of proteins.</p> Signup and view all the answers

    What is the first step in converting text sequences into a numerical representation?

    <p>Tokenize the sequence</p> Signup and view all the answers

    What do measures of complexity features correlate with?

    <p>The reading speed of a text.</p> Signup and view all the answers

    Why is it beneficial to order tokens in the vocabulary by frequency?

    <p>It aids in identifying patterns and insights in the data</p> Signup and view all the answers

    What does the bag-of-words approach result in when representing text documents?

    <p>A sparse representation of each input sequence</p> Signup and view all the answers

    What must be ensured when mapping different lengths of text sequences in their numerical representations?

    <p>All text representations must map to the same dimension in the feature space</p> Signup and view all the answers

    What is the purpose of encoding tokens numerically in the initial text?

    <p>To provide a numeric format that computers can understand</p> Signup and view all the answers

    What is the role of mRNA in an organism?

    <p>To trigger the synthesis of specific proteins.</p> Signup and view all the answers

    What is a k-mer in the context of computational biology?

    <p>A substring of length k in a DNA sequence.</p> Signup and view all the answers

    How can higher-order structures of proteins be represented?

    <p>With a graph indicating interactions between amino acids.</p> Signup and view all the answers

    What defines a tree's structure in the context of graph encoding?

    <p>A tree's structure avoids cycles.</p> Signup and view all the answers

    What can be inferred about the encoding of molecules using graphs?

    <p>Graphs generalize the notion of n-grams beyond sequential arrangements.</p> Signup and view all the answers

    Study Notes

    Lecture 3 - Structured Inputs

    • Lecture date and time: November 9, 2023, 10:05 AM

    Part I - Designing Features

    • Aim: Encode input objects (like text, images, DNA) into numerical values to distinguish them.
    • Focus on features that highlight key differences between various objects.
    • Objects with rich representations are ideal targets.
    • Examples: text documents, images, DNA sequences, molecules.
    • Importance is placed on converting sequences (like text or DNA) into understandable numerical formats.

    1.1 Encoding Sequences

    • Text Sequences:
      • Computers do not interpret words as humans do.
      • Therefore, numerical representation for words in text sequences is needed.
      • Steps in conversion: Tokenization, Vocabulary Building, and Numerical Encoding of Texts.
      • Tokenization: Splitting text into individual words (tokens).
      • Build a vocabulary: Collecting all unique tokens (words) in the dataset.
      • Numerical Encoding: Mapping each token to a unique index from the vocabulary in the initial text.
    • Importance of Representations that preserve length: Lengths of text inputs vary, so representations must be the same dimension in feature space. Key is consistent dimensionality for the features.
    • Various Different approaches for encoding texts includes:
      • Bag-of-words (Each token is counted)
      • Zero-one
      • Normalized Frequency

    1.2 Preserving Sequential Order

    • N-grams: Methods that preserve consecutive tokens/words
      • Bigrams: Consider pairs of consecutive words.
      • Trigrams: Consider groups of three consecutive words.
    • Static Features Include:
      • Measurements of complexity: Avg characters/word, avg words/sentence (Correlate with readability).
    • Other Features: Length (Length Features),lexicon counting (external words counts found frequently).

    1.3 Encoding Graphs

    • Represent each atom as a node.
    • Connect atom-atom bounds using undirected edges.
    • Maps bigrams to graphs by visualizing adjacent nodes.
    • Graphs represent n-grams in generalized forms.
    • Leads to combinations of node numbers that expand on consecutive n-grams.

    1.4 Encoding Trees

    • Special cases of graphs.
    • Tree structure excludes cycles, creating unique advantages for encoding.
    • Uses in Modeling: modeling internet conversations, where texts are already structured.
    • Uses "prompt/response" pair features to further structure conversational exchanges which is useful.
      • (e.g., In studies of online discussions)

    1.5 Encoding Grids

    • Grids/Images represented as 3-dimensional tensors.
    • Pixels represented by vectors/values, often indicating RGB values.
    • Image-patch analysis (neighbouring pixel blocks).
    • Feature analysis: Applying filters and analyzing their results for relevant features can improve image analysis.
      • mathematical treatment of images as flattened 3 dimensional tensors, allow for mathematical operations (e.g.inner products).
    • Additional Features: edges/corners/other visual elements may be extracted for better image analysis

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    Related Documents

    Lecture 3 Notes PDF

    Description

    This lecture focuses on the encoding of various input objects, such as text, images, and DNA, into numerical representations. It emphasizes the importance of feature design to highlight key differences and discusses methods like tokenization and vocabulary building for text sequences. Understand how these processes are crucial for data analysis and interpretation.

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