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Introduction to Word Embeddings
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Welcome everyone! Today, weβre discussing word embeddings and their crucial role in NLP. Can anyone explain what an embedding is?
Isn't it a representation of words in a numerical form?
Exactly, great point! Word embeddings allow words to be represented as vectors in a multi-dimensional space. This helps in processing languages more effectively. Can someone tell me why this is important?
Because machines need to understand human language, which is complex and nuanced!
Right again! Understanding these nuances allows us to apply embeddings in tasks like sentiment analysis or translation.
Static vs. Contextual Embeddings
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Now, letβs differentiate between static embeddings like word2vec and GloVe, and contextual embeddings like ELMo, BERT, and GPT. Who can explain what static embeddings are?
Static embeddings create a fixed vector representation for words regardless of context, right?
Correct! Both word2vec and GloVe fall under this category. However, does anyone know how contextual embeddings change the game?
Contextual embeddings, like ELMo, adapt based on the sentence context, which helps in understanding different meanings!
Exactly! ELMo provides different embeddings for the same word based on its context, crucial for disambiguation in cases like 'bank'.
Understanding Specific Models - word2vec and GloVe
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Letβs dive deeper into word2vec and GloVe. Can someone outline how word2vec functions?
It uses two models. CBOW predicts target words from context words, while Skip-gram predicts context from target words?
Spot on! And what about GloVe?
GloVe uses global word co-occurrence statistics to determine word relationships and generates vectors accordingly.
Exactly right! Understanding these models lays the foundation for tackling more sophisticated models like BERT and GPT.
Introduction & Overview
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Quick Overview
Standard
The section explores both static embeddings like word2vec and GloVe, as well as contextual embeddings such as ELMo and BERT/GPT. It highlights how these models help in understanding the nuances of language, reflecting meanings that vary based on context.
Detailed
Word Embeddings and Representations
In this section, we dive into the critical concept of word embeddings and representations, which underpins various natural language processing applications. Word embeddings are a form of word representation that allows words to be expressed as vectors in a multi-dimensional space. This includes:
Static Embeddings:
- word2vec: Uses two models - Skip-gram and Continuous Bag of Words (CBOW) - to learn word associations from large datasets. The Skip-gram model predicts surrounding words from a target word, while CBOW does the reverse.
- GloVe (Global Vectors for Word Representation): Aggregates global word co-occurrence statistics from a corpus to derive word vectors, emphasizing word relationships based on their global statistical information.
Contextual Embeddings:
- ELMo (Embeddings from Language Models): Produces embeddings that vary depending on the context of the word within the sentence, which is crucial for disambiguating words like 'bank' in "river bank" versus "savings bank".
- BERT (Bidirectional Encoder Representations from Transformers): Utilizes a deep transformer architecture for bidirectional understanding, which means it considers the context from both directions (left and right of the word in the sentence).
- GPT (Generative Pre-trained Transformer): A transformer model that excels in generating coherent and contextually relevant text based on the preceding context.
Understanding these embeddings is crucial for leveraging advanced NLP techniques effectively.
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Static Embeddings
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Static Embeddings:
β word2vec: Skip-gram and CBOW
β GloVe: Global Vectors for word co-occurrence
Detailed Explanation
Static embeddings are fixed representations of words in a vector space. They do not change according to context. The 'word2vec' model creates these embeddings using two primary techniques:
1. Skip-gram: This approach predicts the surrounding words given a target word. For example, if the target word is 'bank', skip-gram would learn the words that frequently appear with it, like 'river' or 'money'.
2. CBOW (Continuous Bag of Words): This method does the opposite; it predicts the target word from the context words.
Another popular static embedding is GloVe, which stands for 'Global Vectors for word co-occurrence.' GloVe captures the relationship between words by analyzing the frequency with which they appear together in a large corpus. This helps create a vector representation that reflects the words' meanings based on their co-occurrence patterns.
Examples & Analogies
Think of static embeddings like a dictionary entry for a word. Just as a dictionary provides a single definition for a word, static embeddings assign a fixed vector that captures the word's meaning without considering the sentence it's used in. For instance, the word 'bank' will have the same vector representation whether it's used in 'the bank of the river' or 'the bank where I deposit money.'
Contextual Embeddings
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Contextual Embeddings:
β ELMo: Varies representations depending on context
β BERT/GPT: Deep transformer-based contextual understanding
Detailed Explanation
Contextual embeddings represent words dynamically, depending on the specific sentence or context in which they appear. This is crucial because many words have multiple meanings.
- ELMo (Embeddings from Language Models): ELMo generates embeddings by using a deep learning model that takes the entire sentence into account. This means the representation of the word 'bank' will change based on what other words are in the sentence. For example, in 'the bank of the river,' the embedding will reflect the meaning related to a geographic feature. In contrast, in the phrase 'I went to the bank,' it will represent the financial institution.
- BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer): These models use advanced transformer architectures to achieve a deep understanding of context. BERT examines entire sentences bidirectionally, capturing the context from both previous and following words, while GPT generates text in a unidirectional manner, predicting the next word in a sequence based on the preceding words.
Examples & Analogies
Think of contextual embeddings like an actor portraying a character in a movie. The actor's performance changes depending on the script, setting, and other characters' actions. Similarly, a word's meaning shifts based on its context in a sentence. For instance, the vibe of 'bank' shifts dramatically when paired with 'money' versus 'river,' much like how an actor's portrayal of a villain might change with different scenes and other characters.
Key Insight
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Key Insight: "Bank" means different things in βriver bankβ vs. βsavings bankβ
Detailed Explanation
This key insight emphasizes the importance of context in understanding language. The word 'bank' serves as an excellent example of a homonym, a word that has multiple meanings based on its usage. Without recognizing context, we could easily misinterpret the message. In language processing, distinguishing meanings based on surrounding words is critical for effective communication and machine understanding.
Examples & Analogies
Consider this: if you heard someone say, 'I'm going to the bank,' you might imagine a financial institution. However, if they say, 'I'm sitting on the bank,' you'd picture a place beside a river. Just as a listener uses context clues to understand the correct interpretation, language models must decipher meaning in similar ways to process and respond accurately.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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word2vec: A model that uses CBOW and Skip-gram to predict word relationships by creating vector representations.
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GloVe: A global co-occurrence statistics method to generate word vectors, focusing on word relationships.
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ELMo: Contextual embeddings that adapt representations based on the sentence context, providing different meanings.
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BERT: A deep learning model that employs bidirectional encoding for a deeper contextual understanding of words.
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GPT: A transformer-based model that generates text by predicting subsequent words based on preceding context.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
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In the phrase 'bank of a river', 'bank' refers to a land alongside a river, while in 'bank account', it refers to a financial institution. This illustrates the significance of contextual embeddings.
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The term 'dog' would have a different vector representation in word2vec based on its surrounding words. For example, 'dog barks' and 'dog is loyal' would influence its learned vector differently.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
π΅ Rhymes Time
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For understanding words, 'vector' is the trick, word embeddings make meanings quick!
π Fascinating Stories
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Imagine two friends, Alex and Bailey. Alex loves to play by the river, while Bailey keeps savings in a bank. One day, they discuss 'bank', which can mean both. As they swap stories, they realize context matters just as it does in language!
π§ Other Memory Gems
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ELMo's memorizing strategy: E-Emphasis on L-Languageβs M-Meaning based on context.
π― Super Acronyms
Remember GloVe as 'Global Occurrences' because it leverages statistics from the entire text!
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: word2vec
Definition:
A framework for learning word embeddings from text by predicting words based on context (CBOW) or predicting context based on a target word (Skip-gram).
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Term: GloVe
Definition:
An unsupervised learning algorithm that generates word embeddings by aggregating global co-occurrence statistics of words.
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Term: ELMo
Definition:
Embeddings from Language Models, an approach that generates word representations based on context.
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Term: BERT
Definition:
Bidirectional Encoder Representations from Transformers, a model that understands context from both directions, improving comprehension.
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Term: GPT
Definition:
Generative Pre-trained Transformer, a model that excels at text generation by predicting the next words based on given context.