What is embedding?
In simple terms, embedding mean representing words or objects as arrays of numbers in a high-dimensional space.
What is a high-dimensional space?
Think of describing a building not just by its height and width, but by every detail inside like each room’s size, color, and furniture arrangement. Each detail adds a new dimension, giving a fuller picture in a high-dimensional space.
What are word embedding and how are they created?
Word embedding are mathematical representations of words used by computers to understand language. Since computers process numbers, converting words into numerical forms is crucial for tasks like understanding text and translating languages. Word embedding capture meanings and relationships based on how words appear in large amounts of text data.
Imagine a vast collection of books, articles, and websites — all written text. From this corpus, a computer analyzes how words are used together. For example, it learns that “good” often appears near words like “better,” “excellent,” and “great.” This observation forms the basis of word embedding.
Contextual Understanding: Each word is examined within a window of surrounding words to determine its meaning and usage patterns.
Numeric Representation: Relationships between words are converted into numerical vectors (arrays of numbers). Similar words in meaning or usage context have closer vectors. For instance, “good” and “better” might be nearby in this numerical space because they often appear in similar sentences.
Do words always have the same embedding vector?
No, an embedding vector isn’t fixed. It’s typically learned during training based on the context it appears in the data. Factors influencing variability include:
Training Data: Different datasets can lead to varied representations (e.g., general text vs. medical texts).
Contextual Variations: A word’s meaning changes with context (e.g., “bank” in “river bank” vs. “financial bank”).
Model Parameters: Architectural choices affect how embedding are optimized.
Fine-tuning: Pre-trained embedding can be adjusted for specific tasks or datasets.
When are Word Embedding Needed?
Word embedding are essential in machine learning whenever we work with text data. Computers don’t understand text directly, so we use word embedding to convert words into numbers. This allows machines to process and analyze text, enabling tasks like understanding language, translating languages, and more.
Example: Understanding Word Relationships
Word embedding can show how words are connected. For example:
Imagine we have a way to turn words like “king” and “queen” into numbers.
In this number world, “king” and “queen” might have numbers that are similar because they are related words.
Example: Understanding Contextual Meanings
Word embedding adapt to different meanings of words based on their context. For instance:
The word “virus” has different meanings in medical and technology domains.
In medicine, “virus” relates to illness and healthcare.
In technology, “virus” refers to malicious software.
Due to these distinct meanings, “virus” will have different embedding in medical and technology contexts. Word embedding enable computers to discern these differences, helping them interpret and use words correctly depending on their context.
Common examples of word embedding models:
Word2Vec
GloVe (Global Vectors for Word Representation) and so on.
Top comments (0)