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    <abstract>abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019. This task aims to promote the development of text mining systems that extract relationships between Microorganism, Habitat and Phenotype entities. In this paper, we propose a novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences. After that, we propose a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph. Our approach is able to extract both intra- and inter-sentence relations, and meanwhile utilize syntax information. The results show that our approach achieved the best F1 (66.3%) in the official evaluation participated by 7 teams.</abstract>
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%0 Conference Proceedings
%T Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
%A Xiong, Wuti
%A Li, Fei
%A Cheng, Ming
%A Yu, Hong
%A Ji, Donghong
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xiong-etal-2019-bacteria
%X abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019. This task aims to promote the development of text mining systems that extract relationships between Microorganism, Habitat and Phenotype entities. In this paper, we propose a novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences. After that, we propose a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph. Our approach is able to extract both intra- and inter-sentence relations, and meanwhile utilize syntax information. The results show that our approach achieved the best F1 (66.3%) in the official evaluation participated by 7 teams.
%R 10.18653/v1/D19-5723
%U https://aclanthology.org/D19-5723/
%U https://doi.org/10.18653/v1/D19-5723
%P 158-167

Markdown (Informal)

[Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs](https://aclanthology.org/D19-5723/) (Xiong et al., BioNLP 2019)

• Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs (Xiong et al., BioNLP 2019)

ACL

• Wuti Xiong, Fei Li, Ming Cheng, Hong Yu, and Donghong Ji. 2019. Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 158–167, Hong Kong, China. Association for Computational Linguistics.