| 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. |
| Outcome: | The proposed framework outperforms the state-of-the-art sequence tagging model on three different tasks. |
Similar Papers
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. |
| Approach: | They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers. |
| Outcome: | The proposed model improves on a set of English-language scientific documents. |
WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
Copied to clipboard
| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
| Outcome: | The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |
A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)
Copied to clipboard
| Challenge: | Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances. |
| Approach: | They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence. |
| Outcome: | The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature. |
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | GraphRel is an end-to-end relation extraction model that uses graph convolutional networks to learn named entities and relations. |
| Approach: | They propose a graph-based relation extraction model which uses graph convolutional networks to jointly learn named entities and relations. |
| Outcome: | The proposed model outperforms previous models on two public datasets: NYT and WebNLG. |
Graphene: a Context-Preserving Open Information Extraction System (C18-2)
Copied to clipboard
| 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 . |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
Copied to clipboard
| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)
Copied to clipboard
| 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. |
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (D18-1)
Copied to clipboard
| Challenge: | Existing dependency-based models neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively. |
| Approach: | They propose an extension of graph convolutional networks that is tailored for relation extraction by pruning dependency trees too aggressively. |
| Outcome: | The proposed model outperforms existing sequence and dependency-based models on the large-scale TACRED dataset and has complementary strengths to sequence models. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
Copied to clipboard
Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |