Start by clarifying the requirements and constraints, then walk through a modular implementation covering tokenization, vocabulary building, count matrix construction, and transform for unseen documents. Finally, discuss extensions like TF-IDF, n-grams, and stop-word removal, emphasizing trade-offs and real-world considerations.
Pro tip: Demonstrate production awareness by discussing how to handle out-of-vocabulary words, memory efficiency for large corpora, and the importance of consistent preprocessing between training and inference.
Ask about corpus size, expected vocabulary size, memory limits, and whether the implementation should be sparse or dense. Clarify if the focus is on correctness, efficiency, or scalability.
Outline the tokenizer (e.g., regex, whitespace), vocabulary builder (mapping tokens to indices, handling OOV), and count matrix representation (e.g., dictionary of dictionaries or sparse matrix).
Describe the fit method to build the vocabulary and count matrix from the corpus, and the transform method to convert new documents using the existing vocabulary, with OOV handling.
Explain how to extend to TF-IDF (weighting), n-grams (capturing context), and stop-word removal (reducing noise). Discuss trade-offs like increased dimensionality vs. improved semantics.
Mention scalability (e.g., using hashing trick, sparse matrices), consistency in preprocessing, and integration with ML pipelines (e.g., scikit-learn API compatibility).
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