CS-7643 Quiz 4 questions and
verified correct answers 100%
guaranteed pass latest 2026-2027
Collobert & Weston Vectors - CORRECT ANSWER-a word and its context is a positive example, a
negative example is placing a random word in the context of the original words. Similar to SVM,
the algorithm uses margin loss to increase the margin between positive and negative examples.
Word2Vec - CORRECT ANSWER-is an algorithm and tool to learn word embeddings by trying to
use a word to predict their context words. context being fixed window of size 2,. Two different
objectives can be used to learn these embeddings: The Skip-Gram objective tries to predict a
context from on a word
Skip-Gram Objective - CORRECT ANSWER-- For each position t=1,...,T predict context words
within a window of fixed size m, given context word w_j.
- Objective function is the avg negative likelihood
How to calculate probability of context word wrt to center word? - CORRECT ANSWER--take the
inner product of U and V to measure how likely word w appears with context word o.
-U when w is a center word
-V when o is a context word
-taking probability is expensive
What are alternatives to probability of context words? - CORRECT ANSWER-1.) Hierarchical
Softmax
, 2.) Negative Sampling
Negative Sampling - CORRECT ANSWER-- idea: for each (w,c) pair, we sample k negative pairs
(w,c')
- maximize the probability that real outside word appears, minimize the probability that random
word appear around the center word
- makes less frequent words to be sampled more often
Connection between Negative Sampling and Collobert & Weston Algorithm? - CORRECT
ANSWER-- NS is similar to collobert and weston's algorithm by training network to distinguish
"good" word context pairs from bad ones
- Collobert & Weston uses margin loss and NS uses probabilistic optimization
Uses of Word Embedding - CORRECT ANSWER-1.). features in down stream ML models
2.) initialization for neural net models for NLP like RNN and SLTM
Other word embeddings - CORRECT ANSWER-1.) GloVe: Global Vectors
2.) fastText: sub-word embeddings
3.) contextualized embedding: elmo and bret
How to evaluate word embedding? - CORRECT ANSWER-1.) Intrinsic Evaluation
2.) Extrinsic Evaluation
Intrinsic Evaluation for word embedding? - CORRECT ANSWER-e.g. Word Analogy Task
1.) evaluate specific/intermediate task
2.) fast to compute
3.) helps to understand system
verified correct answers 100%
guaranteed pass latest 2026-2027
Collobert & Weston Vectors - CORRECT ANSWER-a word and its context is a positive example, a
negative example is placing a random word in the context of the original words. Similar to SVM,
the algorithm uses margin loss to increase the margin between positive and negative examples.
Word2Vec - CORRECT ANSWER-is an algorithm and tool to learn word embeddings by trying to
use a word to predict their context words. context being fixed window of size 2,. Two different
objectives can be used to learn these embeddings: The Skip-Gram objective tries to predict a
context from on a word
Skip-Gram Objective - CORRECT ANSWER-- For each position t=1,...,T predict context words
within a window of fixed size m, given context word w_j.
- Objective function is the avg negative likelihood
How to calculate probability of context word wrt to center word? - CORRECT ANSWER--take the
inner product of U and V to measure how likely word w appears with context word o.
-U when w is a center word
-V when o is a context word
-taking probability is expensive
What are alternatives to probability of context words? - CORRECT ANSWER-1.) Hierarchical
Softmax
, 2.) Negative Sampling
Negative Sampling - CORRECT ANSWER-- idea: for each (w,c) pair, we sample k negative pairs
(w,c')
- maximize the probability that real outside word appears, minimize the probability that random
word appear around the center word
- makes less frequent words to be sampled more often
Connection between Negative Sampling and Collobert & Weston Algorithm? - CORRECT
ANSWER-- NS is similar to collobert and weston's algorithm by training network to distinguish
"good" word context pairs from bad ones
- Collobert & Weston uses margin loss and NS uses probabilistic optimization
Uses of Word Embedding - CORRECT ANSWER-1.). features in down stream ML models
2.) initialization for neural net models for NLP like RNN and SLTM
Other word embeddings - CORRECT ANSWER-1.) GloVe: Global Vectors
2.) fastText: sub-word embeddings
3.) contextualized embedding: elmo and bret
How to evaluate word embedding? - CORRECT ANSWER-1.) Intrinsic Evaluation
2.) Extrinsic Evaluation
Intrinsic Evaluation for word embedding? - CORRECT ANSWER-e.g. Word Analogy Task
1.) evaluate specific/intermediate task
2.) fast to compute
3.) helps to understand system
