ML Unit 3 Quiz

In word embedding a vocabulary of 500 words will have 500 dimension vectors

• True

• False

Tell the one hot encoding for the word students in vocabulary[students play with students]

Binary = true

• 1101

• 1010

• 1110

• 1001

Word2vec can be a

• Model that can be trained from scratch

• Pretrained model

• Both

• none

CBOW and SKIP gram can be trained with

• convolution Neural Network

• Fully connected Layers of ANN

• LSTM RNN

• Recurrent Neural Network

Word2vec is good at

• 1.       Capturing semantic meaning

• 2.       Image classification

• 3     Reducing sparsity and reducing dimensions

• 4.       Both 1 and 3

To remove words like a, an, this, at ... we use

• Lemmatizer

• Stemmer

• Stopwords

• BagofWords

Bag of words in a text preprocessing is a

• Feature Scaling Technique

• Feature Selection Technique

• Feature extraction Technique

• None

Which of these are not english stopwords

• Happy ,Sad

• about, against

• Between, into

• through, durin

Identify the level 1 text preprocessing technique

• Bag of Words

• One Hot Encoding

• Word2vec

• Stemming

What is the distance metric used in text processing

• Euclidean

• Manhattan

• Cosine similary

• Mincowski