Relation Extraction

DataFunTalk

Nov 21, 2022 · Artificial Intelligence

Research on Information Extraction from a Graph Perspective

This presentation reviews the background, significance, current research status, objectives, and key contributions of a graph‑based approach to information extraction, covering entity recognition, relation extraction, event extraction, open‑domain extraction, and the proposed unified modeling framework with experimental results.

Entity RecognitionInformation ExtractionNLP

0 likes · 27 min read

Research on Information Extraction from a Graph Perspective

DataFunSummit

Oct 20, 2022 · Artificial Intelligence

End-to-End Speech Relation Extraction

This paper presents an end‑to‑end approach for extracting relational triples directly from speech signals, bypassing intermediate transcription, and demonstrates its effectiveness on synthesized speech versions of the CoNLL04 and TACRED datasets, highlighting challenges such as length constraints and cross‑modal alignment.

MultimodalNatural Language ProcessingRelation Extraction

0 likes · 17 min read

Laiye Technology Team

Sep 9, 2022 · Artificial Intelligence

Graph Convolutional Networks for Intelligent Document Processing: Principles, Feature Engineering, and Applications

This article presents a comprehensive overview of using graph convolutional networks in intelligent document processing, covering basic GCN theory, adjacency matrix construction, feature engineering—including text, image, and handcrafted features—model architecture, self-supervised training, and real-world applications such as semantic entity recognition and relation extraction.

Intelligent Document ProcessingRelation Extractionfeature engineering

0 likes · 14 min read

Graph Convolutional Networks for Intelligent Document Processing: Principles, Feature Engineering, and Applications

DataFunSummit

Jul 7, 2022 · Artificial Intelligence

Discovering and Enhancing Robustness in Low‑Resource Information Extraction

This article examines the robustness challenges of information extraction tasks such as NER and relation extraction, introduces the Entity Coverage Ratio metric, analyzes why pretrained models like BERT may “take shortcuts,” and proposes evaluation tools and training strategies—including mutual‑information‑based methods, negative‑training, and flooding—to improve model robustness across diverse scenarios.

BERTInformation ExtractionNamed entity recognition

0 likes · 12 min read

Discovering and Enhancing Robustness in Low‑Resource Information Extraction

DataFunTalk

Jan 12, 2022 · Artificial Intelligence

Advances in Knowledge Graph Construction: AI Development, Named Entity Recognition, Relation Extraction, and Attribute Completion

This technical report presents a comprehensive overview of artificial intelligence evolution, knowledge‑graph construction techniques—including traditional, cross‑lingual and reading‑comprehension based named entity recognition, weak‑supervised and joint relation extraction, attribute completion via multi‑source cues, and conditional knowledge‑graph modeling—highlighting recent research findings and experimental results.

AI DevelopmentKnowledge GraphNamed entity recognition

0 likes · 20 min read

Advances in Knowledge Graph Construction: AI Development, Named Entity Recognition, Relation Extraction, and Attribute Completion

DataFunTalk

Jan 9, 2022 · Artificial Intelligence

Information Extraction for Unstructured Text: From Closed to Open

This presentation reviews the concepts, tasks, and challenges of information extraction from unstructured text, covering closed and open settings, relation extraction, joint extraction, and open extraction methods, and discusses recent advances such as segment‑attention, global‑rationale models, ETL, TPLinker, and maximal‑clique based approaches with experimental results.

Information ExtractionKnowledge GraphNatural Language Processing

0 likes · 18 min read

Information Extraction for Unstructured Text: From Closed to Open

DataFunTalk

Dec 1, 2021 · Artificial Intelligence

Awesome Knowledge Graph Resources: Papers, Tools, Datasets, and Projects

This article presents a curated collection of high‑star GitHub "awesome" repositories covering knowledge graph fundamentals, relation extraction, KG‑QA, graph construction, graph neural networks, dynamic graph learning, and multimodal knowledge graphs, providing links, summaries, and key resources for researchers and practitioners.

AI resourcesGraph Neural NetworksKnowledge Graph

0 likes · 12 min read

Awesome Knowledge Graph Resources: Papers, Tools, Datasets, and Projects

DataFunTalk

Mar 22, 2020 · Artificial Intelligence

Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances

This article provides a comprehensive QA‑style review of entity‑relation extraction (ERE), covering pipeline drawbacks, various decoding strategies for NER, common relation‑classification techniques, shared‑parameter and joint‑decoding models, recent transformer‑based approaches, challenges such as overlapping entities, low‑resource settings, and the use of graph neural networks.

Deep LearningNLPRelation Extraction

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Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances

DataFunTalk

Jan 9, 2019 · Artificial Intelligence

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

This article introduces reinforcement learning fundamentals, contrasts it with supervised learning, and explores its challenges and advantages in natural language processing, including applications such as text classification, relation extraction from noisy data, and weakly supervised topic segmentation, while summarizing key insights and experimental results.

Natural Language ProcessingRelation ExtractionText Classification

0 likes · 11 min read

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications