You work for a language translation software company. Your company needs to move from traditional translation software to a machine learning model-based approach that produces the translations accurately. One of your first tasks is to take text given in the form of a document and use a histogram to measure the occurrence of individual words in the document for use in document classification.
Which of the following text feature engineering techniques is the best solution for this task?

Answer options:

A.Orthogonal Sparse Bigram (OSB)
B.Term Frequency-Inverse Document Frequency (tf-idf)
C.Bag-of-Words
D.N-Gram

Answer: C
Option A is incorrect. The Orthogonal Sparse Bigram natural language processing algorithm creates groups of words and outputs the pairs of words that include the first word. You are trying to measure the occurrence of individual words.
Option B is incorrect. Term Frequency-Inverse Document Frequency determines how important a word is in a document by giving weights to words that are common and less common in the document. You are not trying to determine the importance of the words in your document, just the count of the individual words.
Option C is correct. The Bag-of-Words natural language processing algorithm creates tokens of the input document text and outputs a statistical depiction of the text. The statistical depiction, such as a histogram, shows the count of each word in the document.
Option D is incorrect. The N-Gram natural language processing algorithm is used to find multi-word phrases in the text of a document. You are not trying to find multi-word phrases. You are just trying to find the count of the individual words.
Reference:
Please see the article titled Introduction to Natural Language Processing for Text.