Probabilities in NLP: Bigram and Unigram
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Questions and Answers

What is the maximum-likelihood bigram probability PML(Sam | am)?

  • $\frac{3}{5}$
  • $\frac{3}{2}$
  • $\frac{2}{3}$ (correct)
  • $\frac{1}{3}$

    • How many times does 'am' occur in the corpus based on the counts provided?

  • 4
  • 5
  • 3 (correct)
  • 2

    • What does C(am, Sam) represent in the context of calculating PML(Sam | am)?

  • 2 (correct)
  • 3
  • 4
  • 1

    • What is the purpose of calculating the maximum-likelihood unigram probability PML(Sam)?

    <p>To evaluate the overall frequency of a word</p> Signup and view all the answers

    In the expression PML(Sam) = C(Sam) / N, what does N represent?

    <p>The total number of tokens in the corpus</p> Signup and view all the answers

    What is the value of P(0) as given?

    <p>0.91</p> Signup and view all the answers

    How is P(W) calculated from the test set?

    <p>P(0)^9 × P(3)</p> Signup and view all the answers

    What is the total number of digits in the test set?

    <p>10</p> Signup and view all the answers

    How is Perplexity calculated for the test set?

    <p>P(W)^{-N}</p> Signup and view all the answers

    What does P(W) equal when calculated from the test set values provided?

    <p>0.919 × 0.01</p> Signup and view all the answers

    What is the primary purpose of Named Entity Recognition (NER) systems?

    <p>To identify and extract named entities from text</p> Signup and view all the answers

    What differentiates Neural Language Modeling from Statistical Language Modeling?

    <p>Neural models achieve better results by using neural network methods</p> Signup and view all the answers

    Which system is used to transform spoken words into written text?

    <p>Speech-to-Text (STT)</p> Signup and view all the answers

    What technique is primarily used to understand emotions expressed in a piece of text?

    <p>Sentiment Analysis</p> Signup and view all the answers

    How does a language model assign probabilities in natural language processing?

    <p>By predicting the next word based on preceding words</p> Signup and view all the answers

    Which technology is primarily used in chatbots for communication?

    <p>Natural Language Processing (NLP)</p> Signup and view all the answers

    What is the role of parsers in NLP?

    <p>To analyze the syntactic structure of sentences</p> Signup and view all the answers

    What aspect of machine translation is offered through natural language processing?

    <p>Language translation from one language to another</p> Signup and view all the answers

    What is the main advantage of neural smoothing over other smoothing techniques?

    <p>It captures long-range dependencies and semantic similarities.</p> Signup and view all the answers

    Why is smoothing important in language models?

    <p>It ensures all n-grams have non-zero probabilities.</p> Signup and view all the answers

    How does Laplace smoothing modify probability estimation for unseen words?

    <p>It adds one to each count of the words.</p> Signup and view all the answers

    In the context of Laplace smoothing, what is represented by $P_Laplace(w_i)$?

    <p>Smoothed probability estimate for word $w_i$.</p> Signup and view all the answers

    What does the adjusted count $c'_i$ represent in the context of Laplace smoothing?

    <p>The effect of smoothing applied to the word count.</p> Signup and view all the answers

    What is a significant limitation of neural smoothing?

    <p>It is the most advanced technique needing extensive data and resources.</p> Signup and view all the answers

    Which smoothing technique is noted for enhancing the generalization ability of language models?

    <p>Laplace Smoothing</p> Signup and view all the answers

    What challenge does smoothing primarily address in statistical language models?

    <p>Handling unseen n-grams.</p> Signup and view all the answers

    What does Maximum Likelihood Estimation (MLE) use to estimate N-gram probabilities?

    <p>The observed frequency of a prefix</p> Signup and view all the answers

    In the bigram probability example for the sentence 'I am Sam', what is the probability of P(Sam|)?

    <p>0.33</p> Signup and view all the answers

    What does the equation P(wn|wn-1) represent in the context of N-gram modeling?

    <p>The probability of a word given a previous word</p> Signup and view all the answers

    For the bigram probability calculation, what is the significance of the notation C(wn-1 wn)?

    <p>It indicates the occurrence count of the word pair</p> Signup and view all the answers

    How is the probability P(wn|wn-1) computed according to the equations discussed?

    <p>It is the count of the word pair divided by the total occurrences of the sequence</p> Signup and view all the answers

    What is the probability P(am|I) based on the provided bigram calculations?

    <p>0.67</p> Signup and view all the answers

    What is the denominator in the MLE formula used to estimate bigram probabilities?

    <p>C(wn-1)</p> Signup and view all the answers

    Which of the following reflects the general equation for N-gram probability estimation as discussed?

    <p>P(wn|wn-N+1) = C(wn-1 wn) / C(wn-1)</p> Signup and view all the answers

    What is the total number of tokens used in the calculation of PML for 'Sam'?

    <p>18</p> Signup and view all the answers

    What are the weights used for linear interpolation in the probability calculation?

    <p>1</p> Signup and view all the answers

    How many occurrences of the digit '0' are in the training set?

    <p>91</p> Signup and view all the answers

    What is the formula used to calculate the probability of each digit 'd'?

    <p>C(d)/N</p> Signup and view all the answers

    What is the total number of digits in the training set?

    <p>100</p> Signup and view all the answers

    What is the naïve probability estimate for 'Sam' conditioned on 'am' using linear interpolation smoothing?

    <p>12</p> Signup and view all the answers

    How many times does 'Sam' occur in the given count?

    <p>3</p> Signup and view all the answers

    What percentage of the training set is composed of zeros?

    <p>0.91</p> Signup and view all the answers

    Study Notes

    Natural Language Processing (NLP)

    • NLP is a branch of artificial intelligence (AI) enabling computers to understand, generate, and manipulate human language.
    • It combines computational linguistics, machine learning, and deep learning models to process human language.
    • Computational linguistics focuses on understanding and constructing human language models with computers and software tools.
    • Natural language is how humans communicate daily, including speech and text.

    NLP Components

    • Morphological Analysis: Examines word components (prefixes, suffixes, roots).
      • Analyzes word formation and components using machine learning.
    • Lexical Analysis: Breaks down text into fundamental units (words, punctuation, whitespace).
      • Also called tokenization.
      • Splits text into sentences and sentences into words.
    • Syntactic Analysis (Parsing): Analyzes the grammatical structure of a sentence.
      • Identifies syntactic relationships between words and phrases.
        • Part-of-speech tagging (POS) is a necessary first step.
    • Semantic Analysis: Understands the meaning of words and sentences.
      • Includes named entity recognition (NER), word sense disambiguation, and semantic role labeling.
    • Discourse Integration: Captures the context and coherence across sentences.
      • Coreference resolution is a common task, identifying when different expressions refer to the same entity.
    • Pragmatic Analysis: Understands the intended meaning beyond literal meaning.
      • Interprets idioms, sarcasm, and context-specific implications. 
      • Sentiment analysis tools like VADER assess sentiment.

    NLP Applications

    • Sentiment Analysis: Determines the sentiment expressed (positive, negative, or neutral).
    • Text Classification: Categorizes text into predefined categories.
    • Chatbots and Virtual Assistants: Automates customer interactions.
    • Text Extraction: Extracts specific pieces of information (names, dates, etc.) from text.
    • Machine Translation: Translates text from one language to another.
    • Text Summarization: Creates concise summaries of text.
    • Market Intelligence: Analyzes numerous text sources for insights.
    • Auto-correct: Corrects grammar and spelling errors.
    • Intent Classification: Understands the user's intentions behind a query or statement.
    • Urgency Detection: Determines urgency in a message or request.
    • Speech Recognition: Translates speech to text.

    NLP Techniques

    • Bag-of-Words (BoW): Represents text as a set of word counts.
    • TF-IDF (Term Frequency-Inverse Document Frequency): Determines word importance in a document relative to a corpus.
    • Word Embeddings: Represent words in continuous vector space.
    • Recurrent Neural Networks (RNNs): Designed for sequential data (useful for language modeling).
    • Transformers: Advanced models (like BERT and GPT) are highly effective for many NLP tasks; process entire sequences in parallel.

    NLP Challenges

    • Ambiguity: Words and sentences can have multiple meanings.
    • Context Understanding: Capturing the context of words in different scenarios.
    • Data Quality: High-quality, annotated data is essential for training effective models.
    • Computational Resources: Training deep learning models can be resource-intensive.
    • Model Interpretability: Deep learning models are often difficult to understand internally ("black boxes")

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    Description

    Test your understanding of maximum-likelihood probabilities in natural language processing with this quiz. Questions cover bigram and unigram probabilities, as well as the concepts of counts and perplexity in a corpus. Dive into the fascinating world of language models and their calculations.

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