Streamlining event extraction with a simplified annotation framework
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Summary
This summary is machine-generated.This study introduces an efficient event annotation framework for information extraction, especially beneficial for low-resource languages. Language-specific pretraining and fine-tuning significantly improve entity and relation extraction performance.
Area Of Science
- Natural Language Processing
- Computational Linguistics
- Information Extraction
Background
- Event extraction is a simplified form of relation extraction.
- Existing methods for information extraction face challenges with low-resource languages.
- Universal Dependencies (UD) tagging is a traditional approach for linguistic annotation.
Purpose Of The Study
- To propose an efficient open-domain event annotation framework for information extraction.
- To tailor this framework for low-resource languages.
- To improve entity and relation extraction performance.
Main Methods
- Developed an event annotation method based on event semantic elements.
- Employed language-specific pretraining and task-specific fine-tuning.
- Integrated Universal Dependencies (UD) information during pre-training.
Main Results
- Achieved substantial time-efficiency gains over traditional UD tagging.
- Demonstrated superior performance of language-specific pretraining over multilingual counterparts.
- Obtained F1 scores of 71.16% for entity extraction and 60.43% for relation extraction.
- Showcased improved node classification in a retail banking domain using the extracted event graph.
Conclusions
- The proposed event annotation framework is efficient and effective, particularly for low-resource languages.
- Task- and language-specific fine-tuning are crucial for optimal model performance in information extraction.
- The methodology provides valuable guidance for developing training datasets and enhancing information extraction systems.
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