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Bingyang Ye

5 accepted papers

2025

Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization

NAACL 2025long

Cross-Document Event Coreference (CDEC) annotation is challenging and difficult to scale, resulting in existing datasets being small and lacking diversity. We introduce a new approach leveraging large language models (LLMs) to decontextualize event mentions, by simplifying the document-level annotat…

Cited by 0SourcePDFScholar
2025

Enhanced Noun-Noun Compound Interpretation through Textual Enrichment

EMNLP 2025

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 multiple-choice question. While this setting al

2024

GLAMR: Augmenting AMR with GL-VerbNet Event Structure

COLING 2024main

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…

2024

Linguistically Conditioned Semantic Textual Similarity

ACL 2024long

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 condi…

2023

The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models

ACL 2023findings

We demonstrate that coreference resolution in procedural texts is significantly improved when performing transformation-based entity linking prior to coreference relation identification. When events in the text introduce changes to the state of participating entities, it is often impossible to accur…