In the reading of Natural Language Processing (NLP) applications, we inevitably encounter two main features in action, Categorization and Extraction, and learn how those can be manipulated in so many different ways to effectively address use cases that involve free-form text and the retrieval of information from it. We also hear a lot about Sentiment (which technically is not a separate feature but rather a specialization of the previous two). Finally, we have POS-tagging, which is only occasionally mentioned outside of deeply technical articles for linguists and NLP professionals.
We don’t hear much about other NLP capabilities, and this is mainly because often, depending on how an NLP engine is designed, features beyond Categorization and Extraction are not present at all. Specifically, many NLP solutions today are based on Machine Learning algorithms, and ML rarely delivers great accuracy in problems that require both elevated precision and super-fine identification. Then again, some of these capabilities are incredibly useful, in fact sometimes even the only way to address a particular challenge. In the following, I make the case for one of these not-so-popular NLP tools: SAO (Subject-Action-Object).