| 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. |
Similar Papers
Easy-to-Hard Learning for Information Extraction (2023.findings-acl)
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| Challenge: | Existing models for information extraction (IE) use a one-stage learning strategy to extract the target structure from unstructured text data. |
| Approach: | They propose a unified easy-to-hard learning framework that mimics the human learning process by breaking down the learning process into multiple stages. |
| Outcome: | The proposed framework enables the model to acquire general IE task knowledge and improve its generalization ability on 13 out of 17 datasets. |
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |
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. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
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| Challenge: | a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web. |
| Approach: | This tutorial examines methods for extracting information from the World Wide Web . it explores the commonalities in the challenges and solutions developed to address these different forms of text . |
| Outcome: | This paper examines the commonalities in the challenges and solutions developed to address the World Wide Web. |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
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| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
Cost-effective End-to-end Information Extraction for Semi-structured Document Images (2021.emnlp-main)
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| Challenge: | a real-world information extraction system for semi-structured document images often involves a long pipeline of multiple modules, which can lead to unstable performance if not designed carefully. |
| Approach: | They propose to use a sequence generation task to build an end-to-end IE system . they propose to combine three manually engineered modules with one data-driven module . |
| Outcome: | The proposed system can be easily replaced and deployed in large-scale production. |