## Problem Given a list of text documents and a stopword list, remove all stopword occurrences from each document. Matching is case-insensitive. Preserve original word casing for non-stopwords. Return the filtered documents. ```python def remove_stopwords( documents: list[str], stopwords: list[str] ) -> list[str]: pass ``` ``` Input: documents = ["The quick brown fox", "A dog and a cat"] stopwords = ["the", "a", "and"] Output: ["quick brown fox", "dog cat"] # Extra spaces collapsed, original case preserved for kept words. Input: documents = ["IS THIS WORKING"] stopwords = ["is", "this"] Output: ["WORKING"] ``` ## Follow-ups 1. What data structure do you use for stopword lookup, and why? 2. How do you handle punctuation attached to words (e.g., `"fox,"` where `"fox"` is a stopword)? 3. For a corpus of 10 million documents, how would you parallelize this pipeline? 4. Extend to support language-specific stopword lists that are selected based on detected document language.

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Stopword Filtering: Remove Common Words from Text Efficiently

phone

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Problem Given a list of text documents and a stopword list, remove all stopword occurrences from each document. Matching is case-insensitive. Preserve original word casing for non-stopwords. Return the filtered documents. Follow-ups What data structure do you use for stopword lookup, and why? How do you handle punctuation attached to words (e.g., "fox," where "fox" is a stopword)? For a corpus of 10 million documents, how would you parallelize this pipeline? Extend to support language-specific s…

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This is a reported interview question from a perplexity interview during the phone round.

It covers the following topics: Coding, Phone .

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Coding Phone

Stopword Filtering: Remove Common Words from Text Efficiently

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