14 Questions
What is LSA and how does it approach word representation?
LSA stands for Latent Semantic Analysis. It factorizes the term-document-matrix to obtain lower-dimensional dense features as word representations.
How can we obtain word representations through document occurrences?
One approach is to use the term-document-matrix to represent words based on their occurrences in different documents.
Explain the concept of neighboring words in obtaining word representations.
Neighboring words can be used to create cooccurrence vectors that represent the relationships between words based on their proximity in text.
What is an n-gram?
An n-gram is a subsequence of contiguous items.
Give an example of bigrams in a text.
Example bigrams on text: Introduction to text mining, mining.
How can n-grams be beneficial for other languages?
N-grams are more tolerant to declension and cases, and composed words have a similar vector to the part words.
What is the motivation behind using word embeddings?
The bag-of-words representation does not capture word similarity.
Why do we want word representations with lower dimensionality than our vocabulary?
To have word representations that are of lower dimensionality (100-500) than our vocabulary.
How can we interpret document vectors in the bag of words model?
Document vectors can be interpreted as a term-document matrix.
What is the basic idea behind Word2Vec?
Train a neural network to predict a word given the preceding and following words (CBOW) or predict the preceding and following words given a word (Skip-Gram).
What are the two main configurations for training Word2Vec?
Continuous Bag of Words (CBOW) and Skip-Gram.
How many dimensions are typically used in a single layer network for Word2Vec?
100 to 1000 dimensions.
What is the purpose of mapping every word to a layer in Word2Vec?
To use this layer as a feature for predicting words based on similarity to a 'target' vector.
Does Word2Vec map words or documents to a layer in the neural network?
Words, not documents.
Test your knowledge on word2vec, neural network training, and different models like Continuous Bag of Words (CBOW) and Skip-Gram. Learn about configuring network layers and dimensions for effective training.
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