A team analysing free-text survey responses wants to extract named entities such as organisations, dates, and monetary amounts from each response using an LLM. Conceptually, what does the named entity recognition step add to the raw text that makes the output useful for downstream tabular analysis?
- AIt rewrites each response into a grammatically simpler paraphrase that is easier for humans to read
- BIt translates the response into a single canonical language so all entries share one vocabulary
- CIt locates spans of text and tags each with a type label, turning unstructured prose into typed, extractable fields Correct
- DIt assigns a sentiment polarity score to the response so it can be aggregated numerically
Why A is wrong: Tempting because LLMs can paraphrase, but named entity recognition does not simplify or rewrite the prose; it identifies and labels spans, leaving the surrounding text untouched.
Why B is wrong: Tempting because normalising language aids analysis, but translation is a separate task; named entity recognition operates within the source language and tags spans rather than converting the whole text.
Why C is correct: Correct: named entity recognition detects spans and assigns category labels such as organisation, date, or money, converting unstructured text into typed key-value fields that map cleanly onto columns for tabular analysis.
Why D is wrong: Tempting because both are common text-analytics steps producing structured fields, but sentiment scoring is a distinct task; named entity recognition labels entity spans and does not output a polarity value.