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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/2407.02719 |
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| _version_ | 1866917711720742912 |
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| author | Shao, Qiwei Mo, Fengran Nie, Jian-Yun |
| author_facet | Shao, Qiwei Mo, Fengran Nie, Jian-Yun |
| contents | Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document. Recent advancements have adapted pre-trained language models for this task. However, the scarcity of domain-specific data and the deviation of concepts from their canonical names often hinder these models' effectiveness. To tackle this issue, we employ MetaMapLite, an existing rule-based concept mapping system, to generate additional pseudo-annotated data from PubMed and PMC. The annotated data are used to augment the limited training data. Through extensive experiments, this study demonstrates the utility of a manually crafted concept mapping tool for training a better concept extraction model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation Shao, Qiwei Mo, Fengran Nie, Jian-Yun Computation and Language Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document. Recent advancements have adapted pre-trained language models for this task. However, the scarcity of domain-specific data and the deviation of concepts from their canonical names often hinder these models' effectiveness. To tackle this issue, we employ MetaMapLite, an existing rule-based concept mapping system, to generate additional pseudo-annotated data from PubMed and PMC. The annotated data are used to augment the limited training data. Through extensive experiments, this study demonstrates the utility of a manually crafted concept mapping tool for training a better concept extraction model. |
| title | Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.02719 |