Papers › Attention Guided Graph Convolutional Networks for Relation Extraction

Attention Guided Graph Convolutional Networks for Relation Extraction

18 Jun 2019ACL 2019 7arXiv:1906.07510archive 2025-07-28

Zhijiang Guo, Yan Zhang, Wei Lu

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.

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Cartus/AGGCN_TACRED officialmentioned in papermentioned on GitHubpytorchMIT report
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clones Cartus/AGGCN_TACRED/PubMed/Binary/model/aggcn.py official repository ran · our draft was wrong MIT (permissive) · 6cdff690e29fd5e2 · report
attention Cartus/AGGCN_TACRED/model/aggcn.py official repository unverified MIT (permissive) · d29688d33b9aa937 · report
batched_index_select Cartus/AGGCN_TACRED/semeval/model/aggcn.py official repository unverified MIT (permissive) · f091cdd21c103f22 · report
head_to_tree Cartus/AGGCN_TACRED/model/tree.py official repository unverified MIT (permissive) · 66027a712727c031 · report
load_tokens Cartus/AGGCN_TACRED/prepare_vocab.py official repository unverified MIT (permissive) · ebfa4cdc3d3f34b8 · report
load_tokens Cartus/AGGCN_TACRED/semeval/prepare_vocab.py official repository unverified MIT (permissive) · 83e7bab03dc7c999 · report
rnn_zero_state Cartus/AGGCN_TACRED/model/aggcn.py official repository unverified MIT (permissive) · f5b0ce312a47a4cf · report
score Cartus/AGGCN_TACRED/utils/scorer.py official repository unverified MIT (permissive) · 9cb44938e773d88b · report
score Cartus/AGGCN_TACRED/semeval/utils/scorer.py official repository unverified MIT (permissive) · c4b42e34eb5cc2e9 · report
tree_to_adj Cartus/AGGCN_TACRED/model/tree.py official repository unverified MIT (permissive) · e47b743b525d74fe · report
tree_to_dist Cartus/AGGCN_TACRED/model/tree.py official repository unverified MIT (permissive) · b836539d7a29d214 · report
unpack_batch Cartus/AGGCN_TACRED/model/trainer.py official repository unverified MIT (permissive) · 24ca48b2ae4c7be9 · report
unpack_batch Cartus/AGGCN/semeval/model/trainer.py community (archive-listed) unverified MIT (permissive) · 80ad2c5df3e82bb4 · report

Tasks

Relation ExtractionSentence

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Relation Extraction TACRED C-AGGCN F1 68.2 #27 of 40 Archive leaderboard report
Relation Extraction TACRED AGGCN F1 65.1 #37 of 40 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Graph Convolutional Networks

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