Building on Machine Learning and Classical AI to Achieve Semantic Understanding
Over the last few years, there has been an on-going, vigorous debate regarding the future of artificial intelligence (AI) and machine learning (ML), and what needs to be developed. The debate comes down to using only machine learning technologies (based on different mathematical models and performing correlation/pattern analysis) versus using a combination of machine learning and "classical AI" (i.e., rules-based and expert systems). (Note that no one believes that rules-based systems alone are enough!) You can read about those debates in numerous articles (such as in the MIT Technology Review, ZDNet's summary of the December 2020 second debate, and Ben Dickson's TechTalks).
Given my focus on knowledge engineering, I tend to land on the side of the "hybrid" approach (spearheaded by Gary Marcus in the debates) that combines ML and classical AI, and then I add on ontologies (to provide formal descriptions of the semantics of things and their relationships and rules).
As a bit more background, Stephen Pinker's book, How the Mind Works, describes this hybrid approach when explaining how people think. "People think in two modes [fuzzy stereotypes with correlations, and systems of rules]. They can form fuzzy stereotypes by uninsightfully soaking up correlations among properties, taking advantage of the fact that things in the world tend to fall into clusters (things that bark also bite and lift their legs at hydrants). But people can also create systems of rules - intuitive theories - that define categories in terms of the rules that apply to them, and that treat all the members of the category equally ... Rule systems allow us to rise above mere similarity and reach conclusions based on explanations."
Pinker later makes this more explicit when he writes, "We treat [things like] games and vegetables as categories that have stereotypes, fuzzy boundaries and family-like resemblances. That kind of category falls naturally out of pattern-associator neural networks. We treat [other things like] odd numbers and females as categories that have definitions, in-or-out boundaries and common threads running through the members. That kind of category is naturally computed by systems of rules."
We can now ask how Deep Narrative Analysis (DNA) uses this hybrid approach and combines machine learning and natural language processing components, classical AI/rules and ontologies. The figure below shows how DNA brings these technologies together. The entities in grey are based on existing ML, ontology and rules-based tooling, while the entities in yellow are developed by OntoInsights to provide semantically-rich understanding.
- Part of speech tagging
- Finding relationships between words/clauses of a sentence (as found with dependency parsing)
- Named entity recognition of persons, organizations, places, events, dates, etc.
- Correlation of words from a corpus (such as accomplished using GPT-2 and -3)