Recap and Today’s Theme
Hello! In the previous episode, we discussed TF-IDF, a method for evaluating word importance based on word frequency in documents and across a corpus. While TF-IDF is effective for many applications, it does not account for the semantic relationships between words, limiting its ability to capture context.
Today, we will explore Word Embeddings, a technique that overcomes these limitations. Word embeddings represent words as vectors in a continuous, low-dimensional space, capturing semantic similarities and relationships. In this episode, we will explain the basic concept of word embeddings, specific methods, and popular algorithms used for creating them.
What are Word Embeddings?
1. Basic Concept of Word Embeddings
Word Embeddings are a technique used to represent words as continuous, low-dimensional vectors that capture their semantic characteristics. This approach assigns each word a fixed-length numerical vector, learned in such a way that words used in similar contexts are placed close together in the vector space. Word embeddings allow models to capture the semantic similarity and relationships between words.
For example, the words “dog” and “cat” are semantically similar, so their corresponding vectors will be positioned close to each other. In contrast, unrelated words like “dog” and “car” will be far apart in the vector space.
2. Differences from BoW and TF-IDF
Both BoW and TF-IDF vectorize documents based on word frequency, but they do not capture semantic relationships. For instance, even if “dog” and “cat” appear in similar contexts, these models do not indicate that these words are related.
Word Embeddings, on the other hand, place semantically similar words close together in the vector space, allowing models to incorporate context information into NLP tasks, such as text classification, machine translation, and sentiment analysis, thereby improving accuracy.
Methods for Creating Word Embeddings
1. Distributional Hypothesis Approach
The basic idea behind word embeddings is based on the Distributional Hypothesis, which states that “words with similar meanings appear in similar contexts.” This hypothesis suggests that we can infer the meaning of a word by examining the words around it. Word embeddings are learned by using this surrounding context (i.e., neighboring words) to determine the meaning of a target word.
2. Difference from One-Hot Encoding
One-Hot Encoding represents words as vectors where each element corresponds to a word in the vocabulary. Only one position is set to 1 (the position corresponding to the word), and all others are 0. However, this method has drawbacks:
- It creates high-dimensional sparse vectors, which are computationally inefficient.
- It does not capture the semantic relationships between words.
In contrast, word embeddings use low-dimensional continuous vectors to represent words, reflecting their semantic relationships in the vector space.
Popular Word Embedding Methods
1. Word2Vec
Word2Vec is an algorithm developed by Google to convert words into low-dimensional vectors. It uses two main approaches:
- CBOW (Continuous Bag of Words): Predicts the target word using the surrounding context.
- Skip-gram: Predicts the surrounding words given the target word.
Word2Vec is known for its simple and efficient learning algorithm, which can learn the semantic relationships between words from large-scale text data.
2. GloVe (Global Vectors for Word Representation)
GloVe is a word embedding method developed by Stanford University. It uses a co-occurrence matrix to learn word vectors. By combining co-occurrence information from the entire vocabulary, GloVe generates vectors that reflect the global statistical properties of words, capturing their semantic relationships.
GloVe differs from Word2Vec in that it leverages global co-occurrence information, making it effective at capturing information about the entire corpus.
3. FastText
FastText, developed by Facebook, represents words as a set of subwords (character n-grams). This approach allows it to generate vectors even for words not present in the training data (out-of-vocabulary words).
For example, the word “running” can be split into subwords like “run”, “nni”, and “ing”. By learning from these subwords, FastText can effectively handle inflections and misspellings.
4. BERT (Bidirectional Encoder Representations from Transformers)
BERT is a language model based on a bidirectional transformer architecture, capturing context from both directions. BERT’s word embeddings are context-dependent, meaning that the same word can have different vectors depending on its context.
For example, the word “bank” can mean “riverbank” or “financial institution” depending on the sentence. BERT assigns different vectors based on the surrounding words, enabling more precise understanding in NLP tasks. It is one of the most advanced technologies in NLP, achieving high performance in natural language understanding tasks.
Applications of Word Embeddings
1. Text Classification
Word embeddings are frequently used in text classification tasks. By representing words as vectors that capture semantic meaning, these vectors are used as features in machine learning models (e.g., neural networks or support vector machines) for tasks like news categorization or spam detection.
2. Machine Translation
Word embeddings play a crucial role in machine translation. By converting words into vector representations and capturing the relationships between languages, it enables more natural translations. Context-aware models like BERT significantly improve translation accuracy.
3. Finding Similar Words
Word embeddings make it easy to find semantically similar words. For instance, using Word2Vec, one can search for words similar to “king” and retrieve related words like “queen”, “emperor”, and “prince”.
4. Sentiment Analysis
Word embeddings are also applied in sentiment analysis. By using vectors that incorporate context information, models can predict the sentiment (positive, negative, neutral) of the entire text more accurately, capturing nuances in expression.
Challenges of Word Embeddings
1. Out-of-Vocabulary Words
Word embeddings cannot generate vectors for words not present in the training data (out-of-vocabulary words). Solutions include subword-based methods like FastText or using pre-trained embeddings that cover a wide vocabulary.
2. Context Dependency
Traditional methods like Word2Vec or GloVe represent each word with a fixed vector, which does not account for context-dependent meanings (e.g., polysemous words). Models like BERT, which generate context-dependent vectors, address this challenge.
Summary
This episode covered Word Embeddings, explaining how they represent words as low-dimensional continuous vectors reflecting their semantic properties. Unlike BoW or TF-IDF, word embeddings capture relationships between words, enabling more advanced NLP processing. In the next episode, we will dive into the mechanism of Word2Vec, explaining how it learns word similarities and the differences between the CBOW and Skip-gram approaches.
Next Episode Preview
Next time, we will explore Word2Vec, explaining how this model learns word similarities and the differences between its CBOW and Skip-gram methods. Stay tuned!
Notes
- Distributional Hypothesis: The idea that words with similar meanings appear in similar contexts.
- One-Hot Encoding: A method representing words as vectors where only one element is set to 1, and all others are 0.
- Subword-Based Learning: A technique that divides words into subword units to handle unknown words and spelling variations.
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