Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
arXiv:1707.00166
Abstract
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.
EMNLP 2017
References in corpus (4)
- Data Programming: Creating Large Training Sets, Quickly
- Combining Neural Networks and Log-linear Models to Improve Relation Extraction
- CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
- Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Cited by in corpus (10)
- Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
- WRENCH: A Comprehensive Benchmark for Weak Supervision
- A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
- Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision
- CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
- A Neural Transition-based Model for Nested Mention Recognition
- Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
- Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
- Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
- Posterior-regularized REINFORCE for Instance Selection in Distant Supervision