Case context: A team builds a sentiment classifier that takes raw, unannotated text directly as input, without using any parser. For the sentence 'This is a great mop!', the classifier treats every word equally and sometimes misclassifies sentiment because it does not clearly recognize which words like 'great' are most important.

Question: Based on the pipeline described in the source, what change should the team make to their system, and why would it help the classifier better identify important words such as 'great'?

Sample answer: The team should add a parser component before the sentiment classifier. The parser would annotate the text, labeling words such as 'great' as an Adjective and 'mop' as a Noun. Feeding this annotated text into the sentiment classifier would help the algorithm give higher weight to important words like 'great' and lower weight to less important words like 'this,' improving its ability to correctly predict sentiment.

Key points:

• The described pipeline uses a parser before the sentiment classifier.
• The parser annotates important words such as adjectives and nouns.
• Annotated text helps the classifier weight words like 'great' more heavily.
• This weighting helps the classifier ignore less important words such as 'this.'

Rubric: Full credit: response identifies the missing parser step, explains that it annotates important words like adjectives and nouns, and connects this to improved weighting of words such as 'great' in the classifier. Partial credit: response mentions adding a parser but does not explain the weighting benefit. No credit: response does not identify the parser as the needed component.