Question 8What is the Skip-Gram approach?1 pointWord n is learned from a large corpus of words, which a human has labeled.Word n is used to predict the words in the neighborhood of word n.The code for word n is fed through a CNN and categorized with a softmax.Vectors for the neighborhood of words are averaged and used to predict word n.
Question
Question 8What is the Skip-Gram approach?1 pointWord n is learned from a large corpus of words, which a human has labeled.Word n is used to predict the words in the neighborhood of word n.The code for word n is fed through a CNN and categorized with a softmax.Vectors for the neighborhood of words are averaged and used to predict word n.
Solution
The Skip-Gram approach is where word n is used to predict the words in the neighborhood of word n. This method is a part of Word2Vec model for generating Word Embedding. In this approach, the model is trained to predict the context given a word. For example, if we have the sentence "The cat sat on the mat", and we choose "sat" as our word n, the Skip-Gram approach will try to predict 'The', 'cat', 'on', 'the', 'mat' given the word 'sat'. This is done by creating pairs of ('sat', 'the'), ('sat', 'cat'), ('sat', 'on'), ('sat', 'the'), ('sat', 'mat') and training the model on these pairs. The model then learns the context or the 'neighborhood' of each word based on these pairs.
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Briefly explain the types of grammer
In the NLP section, you learned tokenisation, N-gram model and two methods to represent a word, which are one-hot representation and word vector. Match the examples on the left-hand side with the technique, model or method on the right-hand side.Group of answer choices['Could', 'have', 'been', 'much', 'better'][('when','i'),('has','been'),('was','the'),('the','matter'),('if','you')]He is singing: [[1,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0],[0,0,0,1,0,0,0,0,0]]([-0.335107, -0.009982, 0.680831, -0.023788, -0.165798,•••,0.345986, -0.232295, 0.021137,0.08515 , -0.24387])
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