Accompanying Code and Models for Chapter 5 of the PhD Thesis 'Global Inference and Local Syntax Representations for Event Extraction'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This release contains the source code used for Chapter 5 of the PhD thesis 'Global Inference and Local Syntax Representations for Event Extraction'. It contains the implementation of the modular, graph-based event extractor described there.Furthermore, the release contains five models produced with the Graph Convolution Network and an repeated negative undersampling probability of 0.9. These five models can either be used alone, or, as intended, as part of a boostrap aggregating implementation of the event extractor.For more information, see the README.md' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["deep learning","event extraction","GCN","LSTM"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This release contains the source code used for Chapter 5 of the PhD thesis 'Global Inference and Local Syntax Representations for Event Extraction'. It contains the implementation of the modular, graph-based event extractor described there.Furthermore, the release contains five models produced with the Graph Convolution Network and an repeated negative undersampling probability of 0.9. These five models can either be used alone, or, as intended, as part of a boostrap aggregating implementation of the event extractor.For more information, see the README.md' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This release contains the source code used for Chapter 5 of the PhD thesis 'Global Inference and Local Syntax Representations for Event Extraction'. It contains the implementation of the modular, graph-based event extractor described there.Furthermore, the release contains five models produced with the Graph Convolution Network and an repeated negative undersampling probability of 0.9. These five models can either be used alone, or, as intended, as part of a boostrap aggregating implementation of the event extractor.For more information, see the README.md
deep learning; event extraction; GCN; LSTM