This paper introduces a new benchmark dataset and evaluation framework for 'data snapshot extraction,' focusing on identifying and localizing semantically meaningful analytical artifacts within operat…

The authors introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database.

The paper introduces a robust four-stage NLP framework that uses schema-constrained LLMs and ESCO vocabulary to accurately extract and align educational competencies with labor market demands, quantif…

The paper introduces 'infilling extraction' to accurately model training data memorization in Diffusion Language Models (DLMs), finding that bidirectional masking significantly increases the extractab…

The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.

The paper introduces FOSSIL, a new multilingual dataset and specialized workflow designed to significantly improve the extraction of citations embedded within complex footnotes common in law and human…

The paper introduces 'bundesrecht,' an open-source, end-to-end pipeline for processing complex German statutory references, which parses, normalizes, and resolves raw citation strings into structured,…

The paper proposes a neuro-symbolic framework to construct highly consistent knowledge graphs for complex question answering by performing ontology-grounded corrections in a post-extraction stage.

The paper introduces Sieve, a system that uses a large language model (LLM) to generate executable query code from natural language security questions, significantly improving the ability to perform c…

The paper enhances French parsing accuracy by integrating data from a syntactic lexicon and applying word clustering methods to verbs within a Probabilistic Context-Free Grammar framework.

The paper introduces IPO-Mine, a comprehensive toolkit and large-scale dataset designed to enable standardized, multimodal analysis of extremely long and structurally complex Initial Public Offering (…

This paper proposes a lightweight encoder-based MEL solution called FAST-MEL that meets three objectives: high linking accuracy, computational efficiency, and storage efficiency.

The paper proposes a novel KAN-enhanced BiGRU architecture to improve legal document classification and summarization in a low-resource, multilingual setting using Bengali and English legal texts.

The paper demonstrates that relying on strict regular-expression parsing for evaluating LLM-based security log classifiers introduces systematic errors, potentially causing a functional model to appea…

The paper introduces ANCHOR, a schema-agnostic system that constructs knowledge graphs from Cyber Threat Intelligence by dynamically discovering and validating against large ontologies, overcoming lim…

This paper proposes a joint BERT-GNN architecture to systematically extract entities and relationships from diverse historical texts, achieving superior performance over conventional methods.

The paper proposes an aggressive, parameter-efficient method to prune non-essential experts from Mixture-of-Experts (MoE) LLMs, significantly compressing the model while maintaining high machine trans…

The paper introduces a novel, scalable framework to monitor and classify dataset usage within research literature, addressing the current lack of infrastructure for tracking data citations.

OmniRetrieval introduces a unified framework that handles natural language queries across diverse, heterogeneous knowledge sources (text, relational, graphs) by dispatching source-native queries witho…

The paper introduces Dr. DocBench, a difficulty-aware, comprehensive benchmark designed to rigorously test expert-level and challenging document parsing capabilities for VLMs, demonstrating that curre…