Jiagao Lyu's research while affiliated with Beihang University (BUAA) and other places

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Publications (2)

Examples of causal relations. The occurrence of document A (the death...
An overview of structure of the model
A storytree of Kobe Bryant’s death. The Numbers 0 to 10 represent 11...
Comparison of the results of ablation experiments on full datasets and...
A storytree-based model for inter-document causal relation extraction from news articles
Article
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  • Nov 2022
With more and more news articles appearing on the Internet, discovering causal relations between news articles is very important for people to understand the development of news. Extracting the causal relations between news articles is an inter-document relation extraction task. Existing works on relation extraction cannot solve it well because of...
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Fig. 1 Bi-LSTM Structure. The figure displays a part of Bi-LSTM...
Fig. 5 Adversarial Multi-task Model (ADV-MTM). The embedding layer and...
Fig. 6 Cross-sharing Multi-task Model (CS-MTM). The embedding layer and...
Biomedical NER datasets used in the experiments
+9 Model Performance Comparison
Multitask learning for biomedical named entity recognition with cross-sharing structure
Article
Full-text available
  • Aug 2019
Background: Biomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, n...
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Citations

... However, the dataset in the biomedical domain is more likely to become unavailable due to the limitations of privacy and specialization. To deal with the above problems, multitask learning (MTL) has been introduced by previous studies [14,28,29,[29][30][31][32][33][34] and achieved great success in the BioNER task. The basic method of MTL is that multiple annotated datasets are trained at the same time to improve the performance on a single dataset. ...
Reference: Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning
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