Papers with semantic framework

5 papers
ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment (2026.eacl-demo)

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Challenge: Existing methods for ESG compliance assessment rely on fact-based retrieval methods.
Approach: They propose a multi-modal information extraction pipeline to extract, structure, and evaluate sustainability reports.
Outcome: The proposed system extracts, structures, and evaluates ESG-related content from text, tables, figures, and infographics.
LGPSolver - Solving Logic Grid Puzzles Automatically (2020.findings-emnlp)

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Challenge: Logic grid puzzle solvers can solve word algebra and logic puzzles in any domain with 100% accuracy.
Approach: They propose a method that uses a DistilBERT-based classifier to classify a clue into one of the predefined predicate types for logic grid puzzles.
Outcome: The proposed solution outperforms state-of-the-art solutions by a large margin on a given dataset.
Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs (2022.emnlp-main)

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Challenge: Existing methods for temporal question answering ignore intrinsic connections between events that can make them temporally related.
Approach: They propose a temporal question answering method that generates query graphs by exploring relevant facts of mentioned entities.
Outcome: The proposed method outperforms existing methods on two benchmarks over different knowledge graphs.
Fine-Grained Temporal Relation Extraction (P19-1)

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Challenge: Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events.
Approach: They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales.
Outcome: The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset .
Generics are puzzling. Can language models find the missing piece? (2025.coling-main)

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Challenge: Generic sentences express generalisations about the world without explicit quantification . human biases in stereotypes can be observed in language models, authors say .
Approach: They analyze generic sentences to determine their quantification and quantify their implicit quantifications using language models.
Outcome: The proposed model shows that generics are more context-sensitive than determiner quantifiers and express weak generalisations.

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