| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
Similar Papers
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
Copied to clipboard
| 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. |
Multi-lingual Entity Discovery and Linking (P18-5)
Copied to clipboard
| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
Learning to Extract Structured Entities Using Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Language Models (LMs) play a pivotal role in extracting structured information from unstructured text. |
| Approach: | They propose to reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. |
| Outcome: | The proposed model outperforms baselines and human evaluations on the extracted entities. |
Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant. |
| Approach: | They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction. |
| Outcome: | The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset. |
Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View (2023.acl-long)
Copied to clipboard
| Challenge: | Special attention is paid to the cross-modal misalignment in text-image datasets which may mislead the learning. |
| Approach: | They propose a multimodal back-translation method which uses diffusion-based generative models for pseudo-paralleled pairs and a divergence estimator to construct a high-resource corpora as a bridge for low-ressource learners. |
| Outcome: | The proposed method outperforms 14 state-of-the-art methods in both entity and relation extraction tasks. |
Social Web Observatory: A Platform and Method for Gathering Knowledge on Entities from Different Textual Sources (2020.lrec-1)
Copied to clipboard
| Challenge: | a framework for gathering entity-centered information is needed in real-life scenarios . a social web observatory system allows users to define their own entities . |
| Approach: | They propose a framework for the collection and summarization of information from the Web in an entity-driven manner. |
| Outcome: | The proposed framework is based on a language analysis pipeline and a human user study. |
Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Document-level entity-based extraction (EE) tasks extract entity-centric information from unstructured text across multiple sentences. |
| Approach: | They propose a generative framework for two document-level EE tasks: role-filler entity extraction (RE) and relation extraction ( RE). |
| Outcome: | The proposed framework captures cross-entity dependencies and avoids exponential computation complexity of identifying N-ary relations. |
Entity Linking in 100 Languages (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multilingual entity linking are cross-lingual, with a focus on zero-shot evaluation. |
| Approach: | They propose a new formulation for multilingual entity linking where language-specific mentions resolve to a language-agnostic Knowledge Base. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large multilingual dataset and shows that frequency-based analysis provided key insights for the model and training enhancements. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |