[amp_mcq option1=”Text vectorization” option2=”Feature scaling” option3=”Part-of-speech tagging” option4=”One-hot encoding” correct=”option3″]
The correct answer is C. Part-of-speech tagging.
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.
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.
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.
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.
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.