Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)
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| Challenge: | Existing Open IE systems focus on identifying and extracting relations of interest, but this manual labor scales linearly with the number of target relations. |
| Approach: | They propose an Open Information Extraction approach that uses a two-layered transformation stage and rhetorical relation identification to transform sentences into syntactically sound sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. |
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Graphene: a Context-Preserving Open Information Extraction System (C18-2)
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| Challenge: | Graphene is an open IE system that generates accurate, meaningful and complete propositions . current systems tend to extract propositions with long argument phrases that can be further decomposed into meaningful propositions, with each of them representing a separate fact. |
| Approach: | They propose a lightweight Open IE system that generates accurate, meaningful propositions . they identify the rhetorical relations that hold between them to maintain their semantic relationship . |
| Outcome: | The proposed system generates propositions that are accurate, meaningful and complete . it preserves the context of the relational tuples extracted from the source sentence . |
GraphIE: A Graph-Based Framework for Information Extraction (N19-1)
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| Challenge: | Most modern Information Extraction (IE) systems are implemented as sequential taggers and model local dependencies. |
| Approach: | They propose a framework that operates over a graph representing a broad set of dependencies between textual units. |
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A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
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New Frontiers of Information Extraction (2022.naacl-tutorials)
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| Challenge: | Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data. |
| Approach: | This tutorial will provide an introduction to recent advances in IE by answering several important research questions. |
| Outcome: | The tutorial will address several important research questions and outline directions for further investigation. |
TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)
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| Challenge: | Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks. |
| Approach: | They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition . |
| Outcome: | The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks. |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
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Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction (2026.acl-srw)
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| Challenge: | Existing approaches to extract relational tuples from text are incomplete and ambiguous . Existing methods rely on predefined schemas to produce t-uples . |
| Approach: | They propose a normalization-first framework that reframes OIE as a structured semantic transformation pipeline . they formalize soundness, completeness, and usefulness as approximate yet verifiable guarantees over extraction quality . |
| Outcome: | The proposed framework aims to make OIE usable for downstream reasoning and machine interpretability. |
Can LLMs Extract Frame-Semantic Arguments? (2025.emnlp-main)
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| Challenge: | Frame-semantic parsing is a critical task in natural language understanding . however, the ability of large language models to extract frame-sensical arguments remains unexplored . |
| Approach: | They propose a framework to extract frame-semantic arguments from large language models . they use JSON representations to enhance performance, but smaller models can achieve competitive results . |
| Outcome: | The proposed model achieves state-of-the-art on ambiguous targets while limiting generalization to out-of domain data. |
Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)
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| Challenge: | a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project. |
| Approach: | They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation . |
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