Word Embeddings and Bag of Words Overview
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Questions and Answers

What is the primary advantage of using subword embeddings like FastText?

  • They improve the speed of model training.
  • They can encode morphological information and handle OOV words. (correct)
  • They eliminate bias in word associations.
  • They effectively handle polysemy.

    • In the context of word embeddings, what does 'polysemy' refer to?

  • The improved speed of model training through word embeddings.
  • The presence of bias in the training data.
  • The ability of a word to have multiple meanings. (correct)
  • The issue of a model being unable to process out-of-vocabulary words.

    • Which task benefits most from word embeddings by capturing semantic similarities, leading to improved word alignment across languages?

  • Information retrieval.
  • Sentiment analysis.
  • Machine translation. (correct)
  • Named Entity Recognition.

    • What is a significant drawback of word embeddings related to training data?

    <p>They can reflect and perpetuate societal biases present in dataset. (C)</p> Signup and view all the answers

    Which method is NOT mentioned as a way to handle the identified challenges of word embeddings?

    <p>Data augmentation to increase data volume. (A)</p> Signup and view all the answers

    What is the primary purpose of word embeddings in Natural Language Processing?

    <p>To transform words into dense vectors that capture semantic relationships. (A)</p> Signup and view all the answers

    Which of the following best describes the Bag of Words (BoW) model?

    <p>A method that represents text as a collection of its unique words, disregarding syntax. (C)</p> Signup and view all the answers

    In the Bag of Words model, what does the 'vector' represent for each document?

    <p>A representation of word counts or occurrences based on a vocabulary. (B)</p> Signup and view all the answers

    What is the essential first step when using Bag of Words on the provided text?

    <p>Tokenize the text into words and build a vocabulary. (C)</p> Signup and view all the answers

    What information does the Bag of Words model primarily preserve about a text?

    <p>The frequency of each word. (D)</p> Signup and view all the answers

    In the provided example, if the vocabulary is [“I”, “love”, “NLP”, “programming”, “in”, “Python”], what would be the vector representation for the document 'I love Python'?

    <p>[1, 1, 0, 0, 0, 1] (D)</p> Signup and view all the answers

    What is the primary purpose of using TF-IDF in text analysis?

    <p>To evaluate the importance of a word in a document relative to a corpus. (A)</p> Signup and view all the answers

    What do word embeddings aim to reflect about words?

    <p>Their semantic similarity based on context. (C)</p> Signup and view all the answers

    In the context of TF-IDF, what does the 'Term Frequency' (TF) measure?

    <p>How often a word appears in a specific document. (D)</p> Signup and view all the answers

    What is the primary goal of using vector arithmetic with word embeddings, as seen in the "analogies" section?

    <p>To understand semantic relationships between words. (B)</p> Signup and view all the answers

    What does the 'Inverse Document Frequency' (IDF) component of TF-IDF primarily measure?

    <p>The rarity or uniqueness of a term across the corpus. (A)</p> Signup and view all the answers

    Which formula correctly represents the calculation of the TF-IDF score for a term 't' in a document 'd'?

    <p>TF(t,d) * IDF(t,d) (D)</p> Signup and view all the answers

    In the context of Word2Vec, what is the main objective of the training process?

    <p>To maximize the likelihood of context words appearing given a target word. (C)</p> Signup and view all the answers

    What is the primary difference between the CBOW and Skip-gram approaches in Word2Vec?

    <p>CBOW predicts the target word from context, and Skip-gram predicts context words from the target word. (A)</p> Signup and view all the answers

    When building word embeddings using the skip-gram model, what is the purpose of selecting a vocabulary size M?

    <p>To simplify the complexity of the embedding task by focusing on the most frequent words (D)</p> Signup and view all the answers

    In the IDF calculation, why is smoothing added when calculating the total number of documents and total number of documents containing a term?

    <p>To avoid division by zero when a term is not present in any document. (D)</p> Signup and view all the answers

    What is the primary purpose of setting a context window size in the skip-gram model?

    <p>To identify the words that are considered context for each target word. (A)</p> Signup and view all the answers

    In the skip-gram model, how is the co-occurrence dictionary built?

    <p>By recording the words that appear within the context window of each target word. (D)</p> Signup and view all the answers

    What is the significance of the embedding size 'N' when creating word embeddings?

    <p>It defines the number of dimensions in each word's vector representation. (D)</p> Signup and view all the answers

    During the training phase of the skip-gram model, what is the goal for positive and negative examples?

    <p>Adjusting target and context word embeddings to outputs close to 1 for positive examples and 0 for negative examples. (A)</p> Signup and view all the answers

    How does GloVe (Global Vectors) differ from Word2Vec?

    <p>GloVe incorporates corpus-level co-occurrences in addition to local context. (C)</p> Signup and view all the answers

    What does the result Vector("king") - Vector("man") + Vector("woman") ≈ Vector("queen") in GloVe embeddings illustrate?

    <p>The encoding of analogies within the embedding space by GloVe. (A)</p> Signup and view all the answers

    What is the primary motivation behind the development of FastText?

    <p>To improve the handling of rare words and subword information. (A)</p> Signup and view all the answers

    How does FastText represent words differently from other methods?

    <p>It represents words as combinations of subword units (character n-grams). (A)</p> Signup and view all the answers

    What does the length of a vector between two words represent in the context of word embeddings?

    <p>The semantic dissimilarity between the words. (D)</p> Signup and view all the answers

    Given the word vectors 'dog' 3,4 and 'cat' 1,1, what is the resulting vector when calculating the difference from 'dog' to 'cat'?

    <p>2, 3 (C)</p> Signup and view all the answers

    Why are word embeddings an improvement over one-hot encoding?

    <p>They capture semantic similarity in a dense, low-dimensional space. (B)</p> Signup and view all the answers

    In the context of one-hot encoding, a categorical variable 'Fruit' with values ['apple', 'banana', 'cherry'] would be represented as vectors such as:

    <p>apple 1, 0, 0, banana 0, 1, 0, cherry 0, 0, 1 (D)</p> Signup and view all the answers

    Which of the following best describes the core limitation of frequency-based word embeddings?

    <p>They do not encode deep semantic relationships between terms. (B)</p> Signup and view all the answers

    Which of these methods is NOT a frequency-based embedding technique?

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

    What is the primary focus of prediction-based word embeddings like Word2Vec?

    <p>Training models to predict context words or target words. (D)</p> Signup and view all the answers

    What is a key characteristic of the vectors of one-hot encoded categorical variable?

    <p>They are sparce and of high dimensionality (C)</p> Signup and view all the answers

    Flashcards

    Context Window Size (C)

    A pre-defined size of text that surrounds a target word to determine its context in a given document.

    Co-occurrence Dictionary

    Records the words that appear within the context window of a target word.

    Embedding Size (N)

    The number of dimensions used to represent a word's meaning in a vector space.

    Skip-gram Model

    Uses gradient descent to adjust word embeddings based on their co-occurrence patterns.

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    GloVe (Global Vectors)

    A method for creating word embeddings based on the frequency of co-occurrences of words within a corpus.

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    FastText

    A word embedding technique that considers subword information.

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    Word Embedding

    A mathematical representation of a word in a multi-dimensional space, representing its meaning.

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    Training

    The process of adjusting the parameters of a model to improve its accuracy.

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    One-Hot Encoding

    A method to represent categorical data as numerical data. Each category is assigned a unique binary vector with all values set to 0 except for a single 1 indicating the presence of that category.

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    TF-IDF (Term Frequency-Inverse Document Frequency)

    A statistical measure that quantifies the importance of a word in a document compared to a collection of documents (corpus). It gives more weight to words that are unique to a document and less weight to common words.

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    Term Frequency (TF)

    The frequency of a term in a document. It is calculated by dividing the count of the term by the total number of terms in the document.

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    Euclidean Distance

    Measuring the distance between two points in a vector space, often used to compare similarity between words.

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    Inverse Document Frequency (IDF)

    A measure of how unique a term is across a collection of documents (corpus). It is calculated using the logarithm of the ratio between the total number of documents and the number of documents containing the term.

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    Frequency-Based Embeddings

    A type of word embedding that focuses on word occurrence and context. Examples include Count Vectorization and TF-IDF.

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    Prediction-Based Embeddings

    Word embeddings created by training models to predict surrounding words or target words. Examples include Word2Vec and GloVe.

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    TF-IDF Score

    The product of Term Frequency (TF) and Inverse Document Frequency (IDF). It combines the frequency of a term in a document with its uniqueness across a corpus, giving a score that represents the importance of the term in the document.

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    Word2Vec

    A neural network model that learns word representations (embeddings) by predicting context words given a target word (CBOW) or predicting the target word given context words (Skip-gram).

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    One-Hot Encoding Vectors

    A type of vector representation where each dimension corresponds to a unique category. These vectors are sparse, meaning most values are zero.

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    Word Embeddings as Dense Vectors

    Dense, low-dimensional vectors that capture semantic information about words, allowing for better understanding of word relationships.

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    Continuous Bag of Words (CBOW)

    The process of predicting the target word in a sentence given the surrounding context words. Example: predict "cat" from "The ... sits".

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    Skip-gram

    The process of predicting the context words in a sentence given the target word. Example: predict "The" and "sits" from "cat".

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    Importance of Word Embeddings

    Word embeddings have revolutionized NLP by enabling machines to understand the meaning of words in context, leading to improvements in various applications.

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    N-Gram Embeddings

    N-gram embeddings capture the meaning of word sequences, like 'cat' or 'the cat'.

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    Out-of-Vocabulary (OOV) Words

    Word embeddings handle words not seen during training, improving model flexibility.

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    Bias in Training Data

    Word embeddings can reflect societal biases present in the training data.

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    Polysemy

    Contextual embeddings, like BERT, address polysemy by considering word context to determine meaning.

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    Bag of Words (BoW)

    A representation of text as a collection of words, ignoring grammar, word order, and syntax, but preserving word frequency.

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    GloVe

    A word embedding technique based on word co-occurrence statistics, capturing the relationships between words based on how often they appear together in a corpus.

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    Textual Data Transformation

    The process of converting text into a numerical format suitable for machine learning algorithms.

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    Analogies By Vector Arithmetic

    The ability of word embeddings to capture semantic relationships between words, allowing for analogies to be performed using vector operations.

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    Semantic Similarity

    The process of identifying words with similar meanings through their proximity in the embedding space.

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    Study Notes

    Word Embeddings

    • Word embeddings represent words as dense vectors.
    • These vectors capture semantic relationships in context.
    • They convert textual data into numerical formats, suitable for machine learning models.
    • Words with similar meanings (e.g., "king" and "queen") are positioned closer in the vector space.

    Bag of Words (BoW)

    • BoW models text as a collection of words, disregarding grammar, word order and syntax.
    • It preserves word frequencies.
    • Text representation is a vector of word counts or occurrences.
    • Vocabulary includes all unique words.
    • Each word gets a unique index.
    • Frequency count for each word in each document.
    • Preprocessing involves tokenization, converting to lowercase and removing punctuation/stopwords.

    Bag of Words (BoW) - How it Works

    • Tokenize the text into words.
    • Normalize the text (convert to lowercase, remove punctuation/stopwords).
    • Collect all unique words from the dataset.
    • Create a vector of word counts for each document, using the vocabulary.

    Bag of Words (BoW) - Example

    • Consider two documents: "I love NLP." and "I love programming in Python."
    • Build the vocabulary: "I", "love", "NLP", "programming", "in", "Python".
    • Count word frequencies.

    Overview

    • Word embeddings are a vector representation of words, capturing semantic relationships.
    • They transform textual data into a numerical format suitable for machine learning models.

    Word Embedding

    • Illustrates words as points in a multi-dimensional space.
    • Shows semantic relationships visually.
    • Points close together represent words with similar meanings.
    • Provides coordinates of words based on gender and age, or age and royalty.

    Analogies by Vector Arithmetic

    • Analogies express relationships between concepts.
    • Calculate the vector relationship between "man" and "king" to derive "woman" and "queen" relationships.

    Measuring Euclidean Distance

    • Calculate the distance between two words by creating a vector and measuring its length.
    • The length is calculated using the formula sqrt(x² + y²).

    Why Are Word Embeddings Important?

    • Traditional NLP relied on sparse, high-dimensional vectors (one-hot encoding).
    • Word embeddings enhance machine translation, sentiment classification, and document retrieval.

    One-Hot Encoding

    • Converts categorical data (e.g. color) into numerical format for machine learning algorithms.
    • Each category gets a unique binary vector (e.g. "Red" = [1, 0, 0]; "Blue" = [0, 1, 0]).

    Types of Word Embeddings

    • Frequency-based: E.g., Count Vectorization, TF-IDF. Focus on word co-occurrence and context but do not capture deep semantic relationships.
    • Prediction-based: E.g., Word2Vec (CBOW, Skip-Gram), GloVe, FastText. Train models to predict surrounding words and capture richer semantic information.

    Term Frequency-Inverse Document Frequency (TF-IDF)

    • A statistical measure to evaluate word importance in a document relative to a corpus.
    • Highlights words unique to a document, reducing the impact of common words.
    • Used for tasks such as search engines, text summarization, spam filtering, and recommendation systems.

    Term Frequency-Inverse Document Frequency (TF-IDF) - How it Works

    • Term Frequency (TF): Measures how often a term appears in a document.
    • Inverse Document Frequency (IDF): Measures how unique a term is across the entire corpus.
    • TF-IDF Score: Combines TF and IDF values to determine term importance in a document.

    Example of TF-IDF Calculation

    • Calculates TF-IDF for the term "cat" in documents "The cat sat on the mat" and "The dog barked at the cat".
    • Uses appropriate formulas for calculating TF and IDF.

    Word2Vec

    • Predicts context words given a target word (CBOW).
    • Predicts the target word given context words (Skip-gram).
    • Aims to maximize the probability of context words appearing, given the target word.

    How Word Embeddings are Created- Skip-gram

    • Build a text corpus (like Wikipedia, news, or Shakespeare's works).
    • Select a vocabulary size, keeping frequent words.
    • Set a context window size.
    • Build a co-occurrence dictionary.
    • Select embedding size (number of dimensions).
    • Create two tables (E for target words, U for context words) initialized with random numbers.
    • Train using gradient descent, adjusting embeddings based on corpus and adjusting positive/negative examples.

    GloVe (Global Vectors)

    • Uses word co-occurrence matrices across the entire corpus.
    • Captures both local (contextual) and global (corpus-wide) statistical information.
    • Incorporates corpus-level co-occurrences.
    • Examples illustrate how it captures relationships like "king"-"man"+"woman"≈"queen".

    FastText

    • Improves handling of rare words and subword information.
    • Represents words as combinations of subword units (character n-grams).
    • Builds embeddings for n-grams and combines them.
    • Handles out-of-vocabulary words.
    • Encodes morphological information.

    Example Applications

    • Enables models to analyze sentiment, translate text, and improve information retrieval.

    Challenges

    • Bias: Embeddings can reflect and perpetuate societal biases in the data (e.g. gender bias).
    • Polysemy: Words with multiple meanings can confuse models, needing more context-aware embedding methods.
    • Out-of-Vocabulary Words: Embeddings may struggle with unseen words, demanding strategies like subword embedding or context-aware models.

    Conclusion

    • Word embeddings are crucial for modern NLP.
    • They enable dense, semantic-rich representations, enabling sentiment analysis, translation, and improved information retrieval.
    • Addressing biases and polysemy is important for better performance.

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    Introduction to Word Embeddings PDF

    Description

    This quiz covers the essential concepts of Word Embeddings and Bag of Words (BoW) models in Natural Language Processing. You will learn how words are represented as vectors and how BoW preserves word frequencies while disregarding grammar. Test your understanding of these fundamental techniques used in machine learning.

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