[amp_mcq option1=”Text vectorization” option2=”Feature scaling” option3=”Part-of-speech tagging” option4=”One-hot encoding” correct=”option3″]<\/p>\n
The correct answer is C. Part-of-speech tagging.<\/p>\n
Part-of-speech tagging (POS tagging) is the process of assigning a part of speech tag to each word in a text. Part of speech tags are used to identify the grammatical role of a word in a sentence. For example, the word “dog” can be tagged as a noun, the word “walk” can be tagged as a verb, and the word “is” can be tagged as a verb.<\/p>\n
POS tagging is a fundamental task in natural language processing (NLP). It is used in a variety of NLP applications, such as machine translation, text summarization, and question answering.<\/p>\n
A. Text vectorization is the process of converting text into a vector representation. This is done by representing each word in the text as a vector of features. The features can be based on the word’s frequency, its position in the text, or its part of speech.<\/p>\n
B. Feature scaling is the process of normalizing the features in a vector representation. This is done to ensure that all of the features have the same scale.<\/p>\n
D. One-hot encoding is a technique used to represent categorical data as vectors. In one-hot encoding, each category is represented by a binary vector. The vector has one bit for each category, and the bit is set to 1 if the data point belongs to the category and 0 otherwise.<\/p>\n","protected":false},"excerpt":{"rendered":"
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