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Questions and Answers
What is the main idea behind Word2Vec?
What is the main idea behind Word2Vec?
- Ignoring context words in a text corpus
- Randomly selecting central words in a text corpus
- Analyzing a huge text corpus with a sliding window (correct)
- Counting the frequency of words in a text corpus
What does Word2Vec compute for the central word in the sliding window?
What does Word2Vec compute for the central word in the sliding window?
- Definitions of the central word
- Probabilities of context words (correct)
- Antonyms of the central word
- Synonyms of the central word
What does Word2Vec adjust to increase the probabilities of context words?
What does Word2Vec adjust to increase the probabilities of context words?
- Vectors (correct)
- Context words
- Sliding window size
- Text corpus length
What is one of the simplest topic models mentioned in the text?
What is one of the simplest topic models mentioned in the text?
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In the context of topic models, what can be used to measure similarity between documents?
In the context of topic models, what can be used to measure similarity between documents?
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In the discussed approach, what is the additional interest besides word vectors?
In the discussed approach, what is the additional interest besides word vectors?
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Study Notes
Word Embeddings and Topic Models
- The main idea behind Word2Vec is to capture the meaning of words by representing them as vectors in a high-dimensional space.
Word2Vec Computation
- Word2Vec computes a vector representation for the central word in the sliding window, taking into account its context words.
Word2Vec Training
- Word2Vec adjusts the vector representations of words to increase the probabilities of context words, given the central word.
Simple Topic Models
- One of the simplest topic models mentioned is Latent Dirichlet Allocation (LDA).
Document Similarity
- In the context of topic models, similarity between documents can be measured using the cosine similarity of their topic distributions.
Word Vectors and Topics
- Besides word vectors, the discussed approach also considers topics, adding an additional layer of meaning to word representations.
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