Work

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Year: 2018

Type: conference-paper

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. 1 Extracting nodes (entities) The SCIIE model extracts entities, their relations, and coreference

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d18-1360

Authors: Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi

Institution: University of Washington

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Allen Institute for Artificial Intelligence, Multidisciplinary University Research Initiative, Office of Naval Research

Awards: III: Medium: Learning Multimodal Knowledge about Entities and Events, G1488683580, G1717380892, RI: Small: Learning to Read, Ground, and Reason in Multimodal Text, G8876996369