NLP: Word Embedding and Document Terms

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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?

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?

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?

The binary values (1: Real Disaster, 0: Not real Disaster) Signup and view all the answers

What are the unique words in the corpus considered as?

Features Signup and view all the answers

What is the primary reason why Machine Learning and Deep Learning algorithms require numeric input?

Because they require numerical numbers to perform tasks Signup and view all the answers

Why do we need word embeddings in Machine Learning and Deep Learning?

To extract knowledge from text data and build useful applications Signup and view all the answers

What is the main difference between Frequency-based and Prediction-based Word Embeddings?

One is based on frequency, the other on prediction Signup and view all the answers

Which of the following is an example of Frequency-based Word Embedding?

Counter Vector Signup and view all the answers

What is the benefit of using word embeddings in Machine Learning and Deep Learning?

It enables the extraction of knowledge from text data Signup and view all the answers

NLP: Word Embedding and Document Terms

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Questions and Answers

What is the primary purpose of word embedding in NLP?

What is a document in the context of NLP?

What is the collection of all the documents present in our dataset called?

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 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?

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?

What is the benefit of using word embeddings in Machine Learning and Deep Learning?

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