more Acrolinx fixes

Author: GraemeMalcolm

learn-pr/wwl-data-ai/get-started-ai-fundamentals/includes/5-natural-language-processing.md

@@ -4,7 +4,11 @@ Key points to understand about natural language processing (NLP) include:
 
 - NLP capabilities are based on models that are trained to do particular types of text analysis.
 - While many natural language processing scenarios are handled by generative AI models today, there are many common text analytics use cases where simpler NLP language models can be more cost-effective.
-- Common NLP tasks include *entity extraction* (identifying mentions of entities like people, places, organizations in a document), *text classification* (assigning document to a specific category) - including *sentiment analysis* (determining whether a body of text is positive, negative, or neutral), and language detection (identifying the language in which text is written).
+- Common NLP tasks include:
+    - *Entity extraction* - identifying mentions of entities like people, places, organizations in a document
+    - *Text classification* - assigning document to a specific category.
+    - *Sentiment analysis* - determining whether a body of text is positive, negative, or neutral and inferring opinions.
+    - *Language detection* - identifying the language in which text is written.
 
 > [!NOTE]
 > In this module, we've used the term *natural language processing* (NLP) to describe AI capabilities derive meaning from "ordinary" human language. You might also see this area of AI referred to as *natural language understanding* (NLU).