Problem facing natural language understanding
Understanding a narrative takes time, especially for complex or layered stories.
The reader has to consider context in understanding the author's intentions, references, or influences; and jot down important details or make highlight marking while reading to remember key aspects of the narrative.
Semantics Relevance Analysis
Semantics Relevance Analysis can be a helpful tool for understanding a narrative. By creating a visual representation, one can gain a clearer overview of the narrative structure and how different elements interact with each other.
However, manual narrative mapping is a mental intensive and tedious task. It is unattainable when proliferation of information is at scale.
ELAINE - Semantic Relevance Analysis
ELAINE is the first automated tool specifically designed to understand narratives.
Instead of relying on rigid parameters and pre-trained models, ELAINE relies on Context Discriminant Calculus to sought out connected stories and nexus, making it possible to automatically conduct narrative mapping without relying on domain specific artifacts; enabling it to provide on-demand succinct narrative map automatically.
From a bird's-eye view to details on the ground, ELAINE gives you the big picture and details with context and semantics in seconds
Finds all the important points
Renders a knowledge map, aligning stories according to context and semantics, making it easy for user to discover mis-aligned information, contradiction, or fallacy
Instant boost to productivity
Domain agnostic - work with textual documents of all subjects
No data model required
ELAINE is not about comprehension or paraphrasing of paragraphs
Automatic abstraction
Hierarchical table of content on abstraction and details
Abstraction hyperlinks to relevant detailed text
Discovers connections between stories, nexus within contexts and semantics to determine relevancy
Augments creative thinking