Papers with biomedical event extraction
Biomedical Event Extraction based on Knowledge-driven Tree-LSTM (N19-1)
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| Challenge: | Biomedical event extraction requires domain-specific knowledge and deep understanding of complex contexts. |
| Approach: | They propose a knowledge base-driven tree-structured long short-term memory networks framework . tree-LSTM framework incorporates dependency structures and entity properties from ontologies . |
| Outcome: | The proposed framework is based on the BioNLP shared task with Genia dataset and achieves state-of-the-art results. |
Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction (2020.lrec-1)
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| Challenge: | Biomedical event extraction systems are evaluated in-domain and on complete event structures only. |
| Approach: | They present a cross-domain study of edge detection for biomedical event extraction . they analyze differences between five existing gold standard corpora and provide a strong baseline model . |
| Outcome: | The proposed model shows a drop in performance when the baseline is applied on out-of-domain data. |
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)
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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He
| Challenge: | Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation. |
| Approach: | They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data. |
| Outcome: | The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature. |
Biomedical Event Extraction as Sequence Labeling (2020.emnlp-main)
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| Challenge: | Empirical results show that BeeSL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios. |
| Approach: | They propose a joint end-to-end neural information extraction model that recasts the task as sequence labeling and jointly models intermediate tasks via multi-task learning. |
| Outcome: | Empirical results show that BeeSL outperforms the current best system on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1 . |