Week1

Which of the options below represent data transformation techniques?

• Neutralization

• One-hot Encoding

• Standardization

• Take-two Encoding

What NLTK function should you use to split a text into individual words?

• word_lemmatizer

• word_stemmer

• word_tokenize

• word_normalize

What function ensures that words like "the" or "is" are excluded from the token list?

• remove_stopwords(tokens)

• stem_tokens(tokens)

• pos_tagging(tokens)

• remove_the(tokens)

• remove_is(tokens)

What method should you use to reduce words to their root form, even if the root form is not an actual word?

• tagging

• lematization

• stemming

• normalization

What method would you use to convert the word "went" to "go"?

• tagging

• normalization

• stemming

• lemmatization

What will be the results of using lemmatization on the word "saw"?

• see

• saw

• seen

• sawing

Answer explanation

The word "saw" is a context-dependent lemma (same word can have different meanings). The word "saw" can function as both a verb and a noun. Lemmatization depends on the part-of-speech (POS) tag to determine the correct base form (lemma).

What NLTK function should you use for labeling each word in a sentence with its corresponding part of speech, such as noun, verb, adjective?

• noun_tag

• noun_label

• pos_label

• pos_tag

What transformations does text normalization involve?

• convert text tot lowercase

• remove punctuation

• remove spaces

• handle special characters

Which OpenCV function converts a BGR image to grayscale?

• cv2.grayscale(image)

• cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

• cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

• cv2.rgb2gray(image)

Why is resizing an image a common preprocessing step in computer vision?

• to enhance the color contrast

• to standardize dimensions for model input

• to reduce computation

• to remove background noise

• to increase image resolution