A law firm's e-discovery team must review thousands of emails collected for a case and automatically flag every mention of a person's name, company name, and date so paralegals can review them for privilege before production. The firm wants software that scans the plain text of each email and picks out these specific categories of words, rather than just searching for a single keyword typed by a user. Which AI capability should the firm use?
Select an answer to reveal the explanation.
Short Explanation
You need named entity recognition because it scans each email and tags person names, company names, and dates as it goes. Keyword search only finds exact words you already type, so it won't catch every name or date. Sentiment analysis and summarization don't label those spans for privilege review.
Full Explanation
The correct capability is named entity recognition, which scans unstructured email text and automatically labels spans as predefined categories such as person names, organization names, and dates, so paralegals can jump straight to those mentions instead of reading every line. That is a natural language processing task built for structured extraction from free text. Keyword search is incorrect because it only returns documents containing an exact term a user types in advance; it cannot discover every person, company, and date across thousands of emails without the reviewer already knowing what to search for. Sentiment analysis is incorrect because it scores the emotional tone of text as positive, negative, or neutral, which has nothing to do with identifying names or dates. Text summarization is incorrect because it condenses a document's overall meaning into a shorter passage, which would blur or drop the very entity details the review team needs to preserve and flag individually.