Towards Discourse Parsing-inspired Semantic Storytelling
원문 초록
Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood!", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs
함께 살펴볼 논문
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RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Cross-Document Contextual Coreference Resolution in Knowledge Graphs
KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition
Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models
토론
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