# Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm

arXiv: https://arxiv.org/abs/2308.11411 · HTML: https://mlsanity.com/paper/2308.11411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases](https://mlsanity.com/paper/1610.08763/) (2016)
- [MetaAID: A Flexible Framework for Developing Metaverse Applications via AI Technology and Human Editing](https://mlsanity.com/paper/2204.01614/) (2022)
- [Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text](https://mlsanity.com/paper/1807.01763/) (2018)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network](https://mlsanity.com/paper/1505.05667/) (2015)
- [MetaOnce: A Metaverse Framework Based on Multi-scene Relations and Entity-relation-event Game](https://mlsanity.com/paper/2203.10424/) (2022)
- [MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models](https://mlsanity.com/paper/2302.13173/) (2023)
- [Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators](https://mlsanity.com/paper/2303.02707/) (2023)
- [Indirect Supervision for Relation Extraction using Question-Answer Pairs](https://mlsanity.com/paper/1710.11169/) (2017)

## Abstract (arXiv)

Extracting relational triples (subject, predicate, object) from text enables the transformation of unstructured text data into structured knowledge. The named entity recognition (NER) and the relation extraction (RE) are two foundational subtasks in this knowledge generation pipeline. The integration of subtasks poses a considerable challenge due to their disparate nature. This paper presents a novel approach that converts the triple extraction task into a graph labeling problem, capitalizing on the structural information of dependency parsing and graph recursive neural networks (GRNNs). To integrate subtasks, this paper proposes a dynamic feedback forest algorithm that connects the representations of subtasks by inference operations during model training. Experimental results demonstrate the effectiveness of the proposed method.