Language Modelling Lecture 1 Quiz
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Questions and Answers

What is the main purpose of language modeling?

  • To translate between different natural languages
  • To model the probability of sequences of words or characters (correct)
  • To analyze the structure and grammar of languages
  • To generate synthetic research papers
  • What is the maximum likelihood solution for a multinomial distribution used to model text data?

  • The probability of each word is equal to the square root of its frequency in the data
  • The probability of each word is equal to the logarithm of its frequency in the data
  • The probability of each word is inversely proportional to its frequency in the data
  • The probability of each word is proportional to its frequency in the data (correct)
  • What is an N-gram model used for in language modeling?

  • To model the probability of entire documents
  • To model the probability of sentences
  • To model the probability of sequences of N words (correct)
  • To model the probability of individual words
  • What is a common application of language modeling mentioned in the text?

    <p>Spell checking</p> Signup and view all the answers

    What is an example of a task that generates synthetic text, as mentioned in the text?

    <p>Natural language generation</p> Signup and view all the answers

    Study Notes

    Language Modelling

    • Language modelling has applications in machine translation, speech recognition, spell checking, natural language generation, and more.

    Introduction to Lecture

    • The lecturer mentions having a warm conversation with the class, which includes "amazing ladies and gentlemen".
    • The lecturer had a delicious tea and mentions the weather (large winds tonight) and a popular book (The tail of two cities).

    Real-World Examples of Language Modelling

    • Google autocompletion is a real-world example of language modelling.
    • Google Translate is another example of language modelling.

    Natural Language Generation

    Modelling Text with Multinomial Distribution

    • Multinomial models the outcome of a single event out of K possibilities.
    • The parameter of a multinomial model is the probability of each possibility.
    • In the context of text data, K represents the vocabulary size.
    • At each position in the text, one of the words in the vocabulary appears.

    Maximum Likelihood Solution

    • Assume word i appeared mi times, and let the total number of words in a document be N.
    • The maximum likelihood solution is calculated based on the probability of each word.

    Probabilistic Modelling

    • Probabilistic modelling is a key concept in language modelling.
    • Conditional probabilities are used in language modelling.

    Language Modelling with N-Grams

    • N-Grams are used in language modelling.
    • Conditionals are an important aspect of language modelling with N-Grams.

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    Description

    Test your knowledge on language modelling including machine translation, speech recognition, natural language generation, and more. This quiz covers concepts like multinomial distribution, Google autocompletion, and Google translate.

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