My first instinct was to just find all matches and slap tags around them, which obviously breaks the moment two patterns overlap.
Clarify requirements first, then propose an efficient algorithm using a trie or Aho-Corasick for multi-pattern matching, followed by interval merging to handle overlaps and adjacency. Discuss time/space complexity and edge cases, and consider practical optimizations for large-scale ML text processing.
Pro tip: Mention that this is a classic multi-pattern matching problem and that Aho-Corasick is the optimal solution for large pattern sets, showing you understand scalability beyond brute force.
Ask about pattern overlap rules, case sensitivity, empty patterns, and whether patterns can contain special characters. Confirm that adjacent matches (e.g., 'ab' and 'bc' in 'abc') should be merged.
For a small number of patterns, a simple scan with each pattern is fine; for many patterns, use Aho-Corasick to find all matches in O(n + m + k) time. Explain the trade-offs.
Use the chosen algorithm to collect start and end indices of every occurrence of every pattern in the string. Store them as intervals [start, end).
Sort intervals by start index, then iterate and merge if the next interval's start is <= current end (adjacent or overlapping). This yields disjoint bold segments.
Build the result by inserting <b> and </b> tags around each merged interval, ensuring no nested or duplicate tags. Return the final string.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.