The punctuation rule is what trips people up.
Start by clarifying the problem constraints and edge cases, then propose an efficient solution using a hash set for banned words and tokenization. Discuss trade-offs like time/space complexity and potential optimizations for large inputs.
Pro tip: Mention that you would preprocess the banned words into a set for O(1) lookups, and consider streaming the messages to handle large datasets without loading everything into memory.
Ask about input sizes, case sensitivity, punctuation handling, and whether messages can be empty or contain multiple spaces. Confirm that tokenization is whitespace-only and matching is case-insensitive.
Propose using a set for banned words (lowercased) and for each message, split by whitespace, lowercase each token, and check against the set. If no token matches, include the message.
Discuss time complexity O(total tokens) and space O(banned words + output). Mention alternatives like regex or trie for partial matching, but note they are unnecessary here.
Address empty messages, messages with only banned words, and large inputs. Suggest streaming or early termination for efficiency.
Walk through a sample input to verify correctness, including case variations and punctuation attached to tokens.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the requirements: phrases are multi-word sequences that must match consecutive tokens in order, case-insensitively, and we need a single left-to-right pass. Then, propose building a trie (prefix tree) from the banned phrases, where each node represents a token and terminal nodes mark the end of a phrase. During the pass, maintain a set of active trie nodes (or a single pointer if using Aho-Corasick) and advance them token by token, reporting matches when a terminal node is reached.
Pro tip: Mention that a naive trie per position is O(n * max_phrase_length) but can be optimized to O(n) by using Aho-Corasick or by maintaining active states; also note that tokenization and case normalization should happen once upfront to avoid repeated work.
Confirm that phrases are sequences of tokens, matching is case-insensitive, and we need a single left-to-right pass. Discuss tokenization (e.g., splitting on whitespace/punctuation) and whether overlapping matches should be reported.
Build a trie from all banned phrases, where each node represents a token and terminal nodes mark phrase ends. For efficiency, consider Aho-Corasick to handle failure links and avoid restarting from scratch at each position.
Iterate over tokens, maintaining a set of active trie nodes (or a single Aho-Corasick state). For each token, transition active nodes to children; if a terminal node is reached, record a match. Add the root's child for the current token to active nodes for starting new matches.
Explain that with a trie and active set, worst-case time is O(n * L) where L is max phrase length, but Aho-Corasick achieves O(n + total matches). Discuss memory trade-offs and whether to precompute or build on the fly.
Consider overlapping phrases, phrases that are prefixes of others, empty tokens, and case normalization. Walk through a small example to verify correctness and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.