Embeddings in Natural Language Processing
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Questions and Answers

Which of the following best describes the primary difference between contextualized and non-contextualized word embeddings?

  • Contextualized embeddings adjust a word's representation based on its surrounding text, while non-contextualized embeddings use a fixed representation for each word. (correct)
  • Contextualized embeddings are more difficult to train than non-contextualized embeddings.
  • Contextualized embeddings can handle out-of-vocabulary words, while non-contextualized embeddings cannot.
  • Contextualized embeddings use mathematical operations to generate vectors, while non-contextualized embeddings rely on a lookup table.

In the context of embeddings, what is 'polysemy' and how do contextualized embeddings address it?

  • Polysemy refers to the process of encoding words into numbers and contextualized embeddings address this by providing a direct one to one mapping.
  • Polysemy refers to words that are related to the same subject, and contextualized embeddings are irrelevant to this problem.
  • Polysemy refers to words with opposite meanings, and contextualized embeddings use complex algebraic equations to solve this problem.
  • Polysemy refers to words with multiple meanings, and contextualized embeddings allow these words to have different representations based on their specific usage. (correct)

Which of the following is NOT a typical use case for word embeddings?

  • Identifying key elements in text like names of people or organizations.
  • Recommending products based on user preferences.
  • Determining the author of a given text. (correct)
  • Translating text from one language to another.

What technique do models like FastText and BERT use to address the challenge of 'out-of-vocabulary' (OOV) words?

<p>Employing subword tokenization, breaking words into smaller, common units. (D)</p> Signup and view all the answers

When searching for similar items using embeddings, which metrics are typically used?

<p>Cosine similarity and dot product similarity. (C)</p> Signup and view all the answers

What is the primary purpose of embeddings in Natural Language Processing (NLP)?

<p>To convert text into a numerical format that machines can understand. (B)</p> Signup and view all the answers

How do embeddings represent semantic meaning in NLP?

<p>By positioning words closer together in a vector space if they have similar meanings. (D)</p> Signup and view all the answers

Which of the following is a type of embedding that focuses on representing entire sentences or phrases?

<p>Sentence Embeddings (B)</p> Signup and view all the answers

What distinguishes contextual word embeddings, like BERT, from non-contextual ones?

<p>Contextual embeddings can give different representations to a word dependent on the sentence it is used in. (B)</p> Signup and view all the answers

Which of the following is an example of a non-contextual word embedding model?

<p>Word2Vec (A)</p> Signup and view all the answers

Which type of embedding would be most appropriate to represent the meaning of an entire research paper?

<p>Document Embeddings (B)</p> Signup and view all the answers

What is a characteristic of non-contextualized embeddings such as GloVe?

<p>They represent each word with a unique embedding that remains constant. (B)</p> Signup and view all the answers

If the word 'bank' is used to refer to a financial institution, and then to the side of a river, which type of embedding would represent these differently?

<p>Contextualized word embeddings, like BERT. (D)</p> Signup and view all the answers

Flashcards

Contextual Embeddings

Word representations that capture the context of the word, meaning the representation changes based on surrounding words. This allows for a more nuanced understanding of language.

Examples of Contextual Embeddings

Models like BERT, GPT, and ELMo that create context-aware word representations.

Polysemy

The ability of a word to have multiple meanings, depending on its context.

Subword Tokenization

A technique used in models like FastText and BERT to break down words into smaller units, improving the handling of unknown words.

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Similarity Metrics (Cosine Similarity, Dot Product Similarity)

Metrics used to measure the similarity between two embeddings, allowing for tasks like finding similar words or documents.

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Embeddings in NLP

A method of representing text as numerical vectors in a high-dimensional space, enabling computers to understand and process language.

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Word Embeddings

Convert words into vectors of numbers that capture their meaning and relationships with other words.

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Sentence Embeddings

Transform sentences into vectors, preserving their context and meaning.

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Document Embeddings

Represent entire documents as vectors to capture their overall semantic meaning.

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Contextual Word Embeddings

Embeddings that consider the context of a word within a sentence, resulting in different representations for the same word based on its usage.

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Non-Contextualized Embeddings

Embeddings that assign the same vector to a word regardless of its context.

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Training Embeddings

The process of training a model to learn meaningful vector representations of words or sentences.

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Embeddings: Why are they important in NLP?

Representing text in a way that captures semantic meaning and relationships, enabling computers to understand language more effectively.

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Study Notes

Embeddings in Natural Language Processing (NLP)

  • Embeddings convert text (words, sentences, documents) into numerical vectors, capturing semantic meaning and relationships.
  • This numerical representation enables machine learning models to perform complex NLP tasks.

Concept of Embeddings

  • Embeddings map words/phrases to vectors in a continuous space.
  • Similarity in meaning corresponds to proximity in the vector space.
  • Contextual meaning is inferred from vector positions.

Types of Embeddings

  • Word Embeddings: Represent individual words (e.g., Word2Vec, GloVe, FastText).
  • Sentence Embeddings: Represent sentences/phrases (e.g., sentence-BERT, Universal Sentence Encoder).
  • Document Embeddings: Represent longer texts (e.g., Doc2Vec).
  • Contextual Word Embeddings: Capture the context of words within sentences (e.g., BERT, GPT).

Contextualized vs. Non-Contextualized Embeddings

  • Non-Contextualized: Static embeddings; the same representation for a word regardless of context (e.g., Word2Vec, GloVe, FastText).
  • Contextualized: Word representations change based on context (e.g., BERT, GPT, ELMo).

Use-cases of Embeddings

  • Sentiment Analysis: Determine sentiment (positive, negative, neutral).
  • Machine Translation: Translate text between languages.
  • Named Entity Recognition (NER): Identify and classify named entities.
  • Information Retrieval: Improve search algorithm relevance.
  • Recommendation Systems: Recommend relevant items/content based on user preferences.

Challenges and Solutions

  • Polysemy (multiple meanings): Addressed by contextual embeddings (e.g., BERT).

  • Out-of-vocabulary (OOV) words: Managed by subword tokenization in models like FastText and BERT.

  • Embeddings enable representation in lower dimensions for various NLP tasks.

  • Choosing between contextual/non-contextual depends on context needs.

  • Similarity searches (cosine similarity, dot product) find similar items efficiently.

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Description

This quiz covers the concept of embeddings in NLP, focusing on how words, sentences, and documents are converted into numerical vectors. You'll explore different types of embeddings, including word, sentence, and document embeddings, and learn about the differences between contextualized and non-contextualized embeddings.

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