Question: Explain how splitting the sentiment classification task into a parser component and a sentiment classifier component can improve the system's ability to predict sentiment accurately, using the example of the word 'great' in your explanation.

Sample answer: By using a parser to first annotate important words such as adjectives and nouns, the pipeline gives the sentiment classifier extra information about which words matter most. For example, in 'This is a great mop!', the parser labels 'great' as an Adjective. This annotation helps the sentiment classifier assign 'great' a higher weight, since adjectives often carry strong sentiment, while giving less weight to less important words such as 'this.' As a result, the classifier can more quickly and accurately hone in on the words that drive the overall sentiment of the text.

Key points:

• Parser annotates important words such as adjectives and nouns.
• Sentiment classifier takes the annotated text as input to predict sentiment.
• Annotating 'great' as an Adjective helps the classifier weight it more heavily.
• This weighting lets the classifier focus on important words and ignore less important ones like 'this.'

Rubric: Full credit: response explains the parser's role in annotating important words, the classifier's role in predicting sentiment from annotations, and how the annotation of 'great' as an adjective leads to higher weighting and improved accuracy. Partial credit: response describes the two components but does not explain how annotation leads to better weighting. No credit: response does not address the pipeline structure or the weighting benefit.