Embedding Space Vector
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An Embedding Space Vector is a vector from an embedding space.
- Context:
- It can (typically) represent Multidimensional Data in a Low-Dimensional Space.
- It can (often) be used to capture Semantic Information or Syntactic Information of various Data Items.
- It can range from being a Word Embedding Vector to being a Graph Embedding Vector.
- It can be used in Machine Learning Models to improve the performance of Classification Tasks and Clustering Tasks.
- It can provide significant improvements in Natural Language Processing applications.
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- Example(s):
- a Text-Item Embedding Vector, such as:
- a Word Embedding Vector that captures its semantic and syntactic information.
- a Sentence Embedding Vector obtained by combining the embeddings of its constituent words or using sentence-level embedding models like BERT.
- a Document Embedding Vector generated by aggregating the embeddings of its words, sentences, or paragraphs.
- an Image Embedding Vector is typically obtained by passing the image through a convolutional neural network and extracting the activations of a specific layer.
- an Audio Embedding Vector often generated using techniques like Mel-frequency cepstral coefficients (MFCCs) or deep learning models.
- a Graph Embedding Vector captures a node's structural and semantic information within the graph context.
- a User Embedding Vector encapsulates a user's preferences, behaviors, and interactions in a recommender system.
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- a Text-Item Embedding Vector, such as:
- Counter-Example(s):
- Euclidean Vectors, which do not necessarily represent data items in an embedding space.
- Matrix, which is a two-dimensional array of numbers not used for embedding purposes.
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- See: Vector Space Model, Deep Learning, Distributional Vector.