Podcast
Questions and Answers
What is the primary purpose of word embedding in NLP?
What is the primary purpose of word embedding in NLP?
- To train hand-built models that use graph embeddings
- To represent words as graphs
- To represent words for text analysis in the form of real-valued vectors (correct)
- To create a corpus of documents
What is a document in the context of NLP?
What is a document in the context of NLP?
- A feature in the corpus
- A single text data point (correct)
- A binary value in a classification task
- A collection of all the documents present in our dataset
What is the collection of all the documents present in our dataset called?
What is the collection of all the documents present in our dataset called?
- A classification task
- A document
- A feature
- A corpus (correct)
What is the target variable in the classification task of predicting which tweets are about real disasters and which ones are not?
What is the target variable in the classification task of predicting which tweets are about real disasters and which ones are not?
What are the unique words in the corpus considered as?
What are the unique words in the corpus considered as?
What is the primary reason why Machine Learning and Deep Learning algorithms require numeric input?
What is the primary reason why Machine Learning and Deep Learning algorithms require numeric input?
Why do we need word embeddings in Machine Learning and Deep Learning?
Why do we need word embeddings in Machine Learning and Deep Learning?
What is the main difference between Frequency-based and Prediction-based Word Embeddings?
What is the main difference between Frequency-based and Prediction-based Word Embeddings?
Which of the following is an example of Frequency-based Word Embedding?
Which of the following is an example of Frequency-based Word Embedding?
What is the benefit of using word embeddings in Machine Learning and Deep Learning?
What is the benefit of using word embeddings in Machine Learning and Deep Learning?
