Explanation of semantic vectors or vector representations
Posted: Sat May 24, 2025 6:37 am
Semantic vectors, also known as vector representations canada phone number list or embeddings, are the basis of machine semantic analysis. A vector is a series of numbers that represent a point in a multidimensional space. Each number in the vector is called a "dimension" and represents a characteristic or property of the word or phrase's meaning.
To understand this better, imagine a map. Each point on the map can be represented in two dimensions: latitude and longitude. Similarly, each word or phrase can be represented as a vector in a multidimensional semantic space. The more similar the meanings of two words are, the closer their vectors are in this space.
Understanding Vector/Embedding Dimensions: Like Map Coordinates
The dimensions of a vector are like the coordinates of a map, but instead of locating a point in physical space, they locate a word or phrase in semantic space. Each dimension represents a characteristic or property of meaning, such as gender, age, sentiment, or any other relevant attribute.
The combination of all dimensions creates a "map" of meaning in semantic space. The more similar the meanings of two words or phrases are, the closer their vectors are in this space.
For example, if a dimension represents sentiment (positive/negative), the words “happy” and “joyful” would have high values on that dimension, while the words “sad” and “depressed” would have low values.
How vectors explain the meaning of texts
Semantic vectors capture the meaning of texts by representing the relationships between words. Words that frequently appear together in similar contexts will have similar vectors. For example, the words "king" and "queen" will have close vectors, as they often appear in similar contexts (monarchy, power, etc.).
Furthermore, since they are numbers, they can be operated on. Imagine we have these same words: King and Queen, and one of the values in our vector measures the gender of the words (it's never that simple, but it serves as an example). It would mean that starting from "King," removing the meaning of "man" and changing it to "woman," we would practically end up with the meaning of Queen.
To understand this better, imagine a map. Each point on the map can be represented in two dimensions: latitude and longitude. Similarly, each word or phrase can be represented as a vector in a multidimensional semantic space. The more similar the meanings of two words are, the closer their vectors are in this space.
Understanding Vector/Embedding Dimensions: Like Map Coordinates
The dimensions of a vector are like the coordinates of a map, but instead of locating a point in physical space, they locate a word or phrase in semantic space. Each dimension represents a characteristic or property of meaning, such as gender, age, sentiment, or any other relevant attribute.
The combination of all dimensions creates a "map" of meaning in semantic space. The more similar the meanings of two words or phrases are, the closer their vectors are in this space.
For example, if a dimension represents sentiment (positive/negative), the words “happy” and “joyful” would have high values on that dimension, while the words “sad” and “depressed” would have low values.
How vectors explain the meaning of texts
Semantic vectors capture the meaning of texts by representing the relationships between words. Words that frequently appear together in similar contexts will have similar vectors. For example, the words "king" and "queen" will have close vectors, as they often appear in similar contexts (monarchy, power, etc.).
Furthermore, since they are numbers, they can be operated on. Imagine we have these same words: King and Queen, and one of the values in our vector measures the gender of the words (it's never that simple, but it serves as an example). It would mean that starting from "King," removing the meaning of "man" and changing it to "woman," we would practically end up with the meaning of Queen.