Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
arXiv:1808.09602
Abstract
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
References in corpus (6)
- End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
- Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
- Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
- Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
- Construction of the Literature Graph in Semantic Scholar
- Learning Global Features for Coreference Resolution