Enhanced Noun-Noun Compound Interpretation through Textual Enrichment

Interpreting Noun-Noun Compounds remains a persistent challenge for Large Language Models (LLMs) because the semantic relation between the modifier and the head is rarely stated explicitly. Recent benchmarks frame Noun-Noun Compound Interpretation as a multiplechoice question. While this setting allows LLMs to produce more controlled results, it still faces two key limitations: vague relation descriptions as options and the inability to handle polysemous compounds. We introduce a dual-faceted textual enrichment framework that augments prompts. Description enrichment paraphrases relations into event-oriented descriptions instantiated with the target compound to explicitly surface the hidden event connecting head and modifier. Conditioned context enrichment identifies polysemous compounds leveraging qualia-role binding and assigns each compound with condition cues for disambiguation. Our method yields consistently higher accuracy across three LLM families. These gains suggest that surfacing latent compositional structure and contextual constraint is a promising path toward deeper semantic understanding in language models. 1