14 Questions
What is a major issue with one-hot encoding of words?
High dimensionality of vectors
What is the main advantage of using Word2Vec over traditional one-hot encoding?
Ability to capture synonymous words
What is the main difference between CBOW and Skip-Gram models?
CBOW is faster to train, while Skip-Gram is more accurate
What is the main limitation of Word2Vec models?
Inability to handle out-of-vocabulary words
What is the main application of Concept Embedding?
Visual image captioning
What is the current state of the art for NLP tasks?
Transformer models
What is the primary motivation for building a better vector representation of words?
To capture the notion of synonymy
What is a benefit of using CBOW over Skip-Gram models?
It is slightly faster to train
What is the purpose of distributional hypothesis in word embeddings?
To capture the meaning of words in context
What is a limitation of traditional one-hot encoding of words?
It results in very big vectors
What is the goal of Concept Embedding?
To learn how images are related to keywords
What is a benefit of using transformer models for NLP tasks?
They are the current state of the art for NLP
What is the purpose of projecting unannotated images into the multidimensional space?
To enable searching by image or keyword
What is the relationship between images and keywords in the multidimensional space?
Images are closer to keywords that describe them
Learn about word embeddings and vector representation, including one-hot encoding, distributional hypothesis, and Word2Vec. Understand how to capture similarity and algebraic semantics in word vectors.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
