Conceptual Dependency Theory (1969) › Limitations: The Brittleness of Hand-Crafted Representations

Despite its theoretical elegance, Conceptual Dependency faced practical limitations that would become evident as systems scaled to more complex domains. The most significant was the need to hand-craft representations. Converting sentences to Conceptual Dependency structures required extensive knowledge engineering: lexical rules mapping words to primitives, case assignment rules determining which entities filled which cases, and inference rules connecting actions to their consequences and prerequisites.

This knowledge engineering proved time-consuming and error-prone. The lexicon had to be manually constructed, with entries specifying how each verb mapped to Conceptual Dependency primitives. But verbs are polysemous. For example, "bank" could mean financial institution or river edge, and the mapping depended on context that wasn't always easy to determine. Creating a lexicon broad enough for unrestricted text required enumerating countless word senses and their mappings, a task that didn't scale.

The choice of primitives also proved problematic. Schank's approximately dozen primitive actions were sufficient for many common events, but some concepts resisted decomposition into these primitives. Complex mental states, subtle social interactions, and abstract concepts often required awkward or unnatural representations using the available primitives. The theory's claim that all events could be decomposed into a small set of primitives worked well for physical actions but seemed less natural for cognitive or social phenomena.

The representation also struggled with ambiguity. Natural language sentences are often ambiguous: structurally ambiguous (different parse trees), lexically ambiguous (different word senses), or scope ambiguous (different quantifier or modifier scopes). A single sentence might map to multiple Conceptual Dependency structures, and the theory provided no principled way to choose among them. Systems relied on heuristics, scripts, or world knowledge to resolve ambiguity, but these weren't part of the core theory.

Inference, while more straightforward than with predicate logic, still required extensive hand-coded rules. Action consequences and prerequisites needed explicit rules, as did longer causal chains. As the domain expanded, the number of inference rules grew, and interactions between rules became difficult to manage. The theory's promise of natural inference from representation structure was only partially realized.

The canonical form assumption, that semantically equivalent sentences should map to identical structures, also proved too strong in practice. "John killed Mary" and "Mary died because of John's actions" express related but not identical meanings. The first emphasizes John's agency, the second emphasizes Mary's death. Sometimes these differences matter for understanding. Forcing everything into a single canonical form could lose important nuances.

Finally, the theory lacked a clear learning mechanism. Unlike later statistical approaches that could learn representations from data, Conceptual Dependency systems required all knowledge to be manually encoded. This made them inflexible and unable to adapt to new domains or usages without extensive reengineering.