Figurative language, the art of deviating from literal meaning for rhetorical effect, is a cornerstone of human communication. Metaphors, similes, irony, and personification enrich our expression, adding layers of nuance and emotional resonance.
The strength of GNNs in tackling figurative language stems from their fundamental ability to represent and reason over interconnected data. Unlike sequential models that process text linearly, GNNs construct a graph representation of the input, where words or concepts become nodes, and the semantic or syntactic relationships between them form edges.
Consider a metaphor like "The internet is an information superhighway." A literal interpretation would focus on the individual meanings of "internet," "information," "super," and "highway." However, the figurative meaning arises from the implicit mapping of characteristics: the internet, like a highway, facilitates the rapid movement of entities (information vs. vehicles), has infrastructure, and connects different locations. GNNs can excel here by explicitly modeling the relationships between these concepts.
Similarly, GNNs are adept at handling simile, which explicitly draws a comparison using "like" or "as." While seemingly simpler, understanding the underlying shared attributes requires identifying the relevant features of both entities being compared. A GNN can represent the two entities as nodes and the features they share as connecting edges, allowing the model to focus on the salient similarities that drive the figurative meaning.
Irony, with its reliance on a contrast between literal and intended meaning, poses a significant challenge for models focused solely on surface-level semantics.
Furthermore, personification, which attributes human qualities to inanimate objects or abstract concepts, benefits from the relational reasoning capabilities of GNNs. Understanding "The wind whispered secrets through the trees" requires recognizing the human action of "whispering" and mapping it onto the sound produced by the wind interacting with trees. A GNN can model the wind and trees as nodes and the "whispered secrets" as a relationship characterized by human-like communication. By learning these types of non-literal attribute transfers across the graph, the model can effectively interpret personified language.
The ability of GNNs to perform reasoning over paths within the graph is also crucial for understanding complex figurative expressions.
The inherent graph-based structure of GNNs makes them exceptionally well-suited for the task of understanding figurative speech. By explicitly modeling the relationships between words and concepts, GNNs can capture the non-literal connections, contextual cues, and underlying mappings that define metaphors, similes, irony, and personification. As research in this area continues to advance, GNNs hold immense promise for enabling AI systems to move beyond literal interpretations and truly grasp the richness and complexity of human figurative language, paving the way for more nuanced and human-like communication.
