10 Questions
What is the main issue with One-Hot Encoding in terms of vocabulary size?
The size of a one-hot vector is directly proportional to the size of the vocabulary, which can be very large in real-world corpora.
What is a major drawback of One-Hot Encoding in terms of word relationships?
It treats words as atomic units and has no notion of (dis)similarity between words.
What is the 'out of vocabulary' (OOV) problem in text representation?
It occurs when a model encounters a word that was not present in the training data.
What is the main idea behind the Bag of Words (BoW) representation technique?
It represents text as a collection of words, ignoring the order and context.
How does the Bag of Words (BoW) technique determine the class of a text piece?
By analyzing the words present in the text, it identifies the class (bag) that the text belongs to.
What is the primary advantage of One-Hot Encoding over Bag of Words (BoW)?
One-Hot Encoding gives a unique representation for each word, while BoW represents text as a collection of words.
How do One-Hot Encoding and Bag of Words (BoW) differ in their treatment of word order?
One-Hot Encoding does not consider word order, while Bag of Words (BoW) ignores word order.
What is the relationship between the size of the vocabulary and the size of a one-hot vector?
The size of a one-hot vector is directly proportional to the size of the vocabulary.
How does the scikit-learn implementation of One-Hot Encoding address the limitations of this technique?
It does not fully address the limitations, which include high dimensionality and lack of word similarity capture.
What is the main benefit of using a fixed-length representation for text?
It allows for efficient comparison and analysis of text samples.
Test your knowledge of natural language processing and its applications in understanding the meaning of sentences. This quiz explores the concepts of syntax, semantics, and vector space models in text representation.
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