Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929682884067328 |
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| author | Noravesh, Farshad Haffari, Reza Fang, Ong Huey Soon, Layki Rajalana, Sailaja Pal, Arghya |
| author_facet | Noravesh, Farshad Haffari, Reza Fang, Ong Huey Soon, Layki Rajalana, Sailaja Pal, Arghya |
| contents | Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the final prediction. To this end, the full adjacency matrix of the dependency graph is used to embed individual tokens using a graph convolutional network(GCN). An ablation study is also done to show the effect of the dependency graph on the overall performance. The results show a significant improvement when dependency graph information is used. The proposed model slightly outperforms state-of-the-art models on BEE over different datasets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_01158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks Noravesh, Farshad Haffari, Reza Fang, Ong Huey Soon, Layki Rajalana, Sailaja Pal, Arghya Computation and Language Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the final prediction. To this end, the full adjacency matrix of the dependency graph is used to embed individual tokens using a graph convolutional network(GCN). An ablation study is also done to show the effect of the dependency graph on the overall performance. The results show a significant improvement when dependency graph information is used. The proposed model slightly outperforms state-of-the-art models on BEE over different datasets. |
| title | Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.01158 |