Work

Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction

Year: 2019

Type: conference-paper

Abstract: In this paper we tackle two unique challenges in biomedical relation extraction.The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures.We propose a novel graph convolutional networks model that incorporates dependency parsing and contextualized embedding to effectively capture comprehensive contextual information.The second challenge is that most of the benchmark data sets for this task are quite imbalanced because more than 80% mention pairs are negative instances (i.e., no relations).We propose a multi-task learning framework to jointly model relation identification and classification tasks to propagate supervision signals from each other and apply a focal loss to focus training on ambiguous mention pairs.By applying these two strategies, experiments show that our model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.

Source: International Workshop on Health Text Mining and Information Analysis (LOUHI)

DOI: 10.18653/v1/d19-6204

Authors: Diya Li, Heng Ji

Institutions: Rensselaer Polytechnic Institute, University of Illinois Urbana-Champaign

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors