Word Embedding Space: Understanding Semantic Relationships in 2D

What Word Embedding Space Is

Word embedding is a technique used in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. In this 2D space, each word is represented by a point, and its position reflects semantic relationships with other words.

The goal of these embeddings is to capture the context and meaning of words such that semantically similar words are close to each other in the vector space.

How Semantic Clusters Form

Semantic clusters form naturally when words with similar meanings are mapped into a high-dimensional space. In our 2D representation, these clusters appear as groups of points that are closer to each other compared to those in different clusters.

This clustering helps in understanding the semantic relationships between words and can be used for tasks such as word analogy solving and similarity-based retrieval.

The King-Man+Woman Analogy

One of the most famous examples demonstrating the effectiveness of word embeddings is the king-man+woman analogy. In a properly trained embedding, adding the vector for 'man' to that of 'king' and subtracting the vector for 'man', should result in a vector close to 'queen'.

This example showcases how linear algebra can be used to solve analogies, which has numerous applications in NLP tasks.

Interacting with the Embedding Space

By interacting with this 2D embedding space, you can explore various aspects of word relationships. For instance, adjusting the neighbor link threshold slider allows you to filter out less relevant connections between words.

Dragging and panning the view or zooming in and out provides a more detailed look at specific areas of interest within the semantic landscape.

Frequently asked questions

Try it live

Everything above runs in your browser — open Word Embedding Space (2D) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.