Instruction: Describe the concept of a bag of words model and its application in NLP.
Context: This question evaluates the candidate's knowledge of simple text representation models.
The way I'd approach it in an interview is this: A bag-of-words model represents a document by the words it contains and usually their counts, while ignoring word order. The output is often a sparse vector where each dimension corresponds to a vocabulary term.
It is simple and surprisingly effective for some classification and retrieval tasks, but it loses syntax, sequence, and nuance. That is its core tradeoff: easy to build and interpret, but weak at capturing deeper language structure.
What I always try to avoid is giving a process answer that sounds clean in theory but falls apart once the data, users, or production constraints get messy.
A weak answer says bag of words stores the important words and misses that it discards word order and context.