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

Jin Zhao, Jingxuan Tu, Bingyang Ye, Xinrui Hu, Nianwen Xue, James Pustejovsky. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between a pair of sentences.In order to reduce the inherent ambiguity posed from the sentences, a recent work called Conditional STS (C-STS) has been proposed to measure the sentences' similarity conditioned on a certain aspect.Despite the popularity of C-STS, we find that the current C-STS dataset suffers from various issues that could impede proper evaluation on this task.In this paper, we reannotate the C-STS validation set and observe an annotator discrepancy on 55% of the instances resulting from the annotation errors in the original label, ill-defined conditions, and the lack of clarity in the task definition.After a thorough dataset analysis, we improve the C-STS task by leveraging the models' capability to understand the conditions under a QA task setting.With the generated answers, we present an automatic error identification pipeline that is able to identify annotation errors from the C-STS data with over 80% F1 score.We also propose a new method that largely improves the performance over baselines on the C-STS data by training the models with the answers.Finally we discuss the conditionality annotation based on the typed-feature structure (TFS) of entity types.We show in examples that the TFS is able to provide a linguistic foundation for constructing C-STS data with new conditions.

This paper introduces GLAMR, an Abstract Meaning Representation (AMR) interpretation of Generative Lexicon (GL) semantic components. It includes a structured subeventual interpretation of linguistic predicates, and encoding of the opposition structure of property changes of event arguments. Both of these features are recently encoded in VerbNet (VN), and form the scaffolding for the semantic form associated with VN frame files. We develop a new syntax, concepts, and roles for subevent structure based on VN for connecting subevents to atomic predicates. Our proposed extension is compatible with current AMR specification. We also present an approach to automatically augment AMR graphs by inserting subevent structure of the predicates and identifying the subevent arguments from the semantic roles. A pilot annotation of GLAMR graphs of 65 documents (486 sentences), based on procedural texts as a source, is presented as a public dataset. The annotation includes subevents, argument property change, and document-level anaphoric links. Finally, we provide baseline models for converting text to GLAMR and vice versa, along with the application of GLAMR for generating enriched paraphrases with details on subevent transformation and arguments that are not present in the surface form of the texts.