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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.14962 |
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| _version_ | 1866912144402939904 |
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| author | Nagayama, Kotaro Kato, Shota Kano, Manabu |
| author_facet | Nagayama, Kotaro Kato, Shota Kano, Manabu |
| contents | The extraction of variable definitions from scientific and technical papers is essential for understanding these documents. However, the characteristics of variable definitions, such as the length and the words that make up the definition, differ among fields, which leads to differences in the performance of existing extraction methods across fields. Although preparing training data specific to each field can improve the performance of the methods, it is costly to create high-quality training data. To address this challenge, this study proposes a new method that generates new definition sentences from template sentences and variable-definition pairs in the training data. The proposed method has been tested on papers about chemical processes, and the results show that the model trained with the definition sentences generated by the proposed method achieved a higher accuracy of 89.6%, surpassing existing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14962 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction Nagayama, Kotaro Kato, Shota Kano, Manabu Computation and Language The extraction of variable definitions from scientific and technical papers is essential for understanding these documents. However, the characteristics of variable definitions, such as the length and the words that make up the definition, differ among fields, which leads to differences in the performance of existing extraction methods across fields. Although preparing training data specific to each field can improve the performance of the methods, it is costly to create high-quality training data. To address this challenge, this study proposes a new method that generates new definition sentences from template sentences and variable-definition pairs in the training data. The proposed method has been tested on papers about chemical processes, and the results show that the model trained with the definition sentences generated by the proposed method achieved a higher accuracy of 89.6%, surpassing existing models. |
| title | Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.14962 |