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Conference Paper
DR based Sentence & SPO Tuple Pair Generation for Open Information Extraction
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- Authors
- Joonyoung Jung, Dong-oh Kang
- Issue Date
- 2023-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1434-1436
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper presents a method for generating sentence and SPO (Subject-Predicate-Object) tuple pairs based on dependency relations (DR) to train a DNN model for open information extraction. The proposed system extracts sentences from the OLLIE dataset that contain seed tuples in NL-based sentences with SPO tuples, performs dependency parsing on the NL-based sentences, and generates DR-based sentence and SPO tuple pairs. Consequently, approximately 230,000 DR-based sentence and SPO tuple pairs were generated from the 3 million data. To demonstrate the effectiveness of the generated DR-based learning data in natural language processing, experiments were conducted using the BERT model to recognize SPO tuples. The results showed that the performance of SPO tuple extraction was better when using DR-based learning data compared to NL-based learning data. Specifically, the average accuracy for top1, top-3, and top-5 was 0.08, 0.25, and 0.22 higher, respectively, when using DR-based learning data compared to NL-based learning data.
- KSP Keywords
- Dependency Parsing, Dependency Relations, Learning data, Natural Language Processing, Open information extraction, Tuple extraction