从论文中提取生物信息学工作流信息，突破低资源限制。
Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows
- 构建专用语料库，用SciBERT做命名实体识别
- 达到70.4的F1值，接近人工标注一致度
- 适合需自动化解析生物文献的研究者
生物信息学工作流对复杂生物数据分析至关重要，常在科研论文中以源代码形式公开。从论文中提取详细的工作流信息可提升数据可访问性与可重用性，但受限于标注语料稀缺。为此，我们将问题定义为低资源信息抽取任务，测试四种策略：1）构建定制化标注语料库；2）使用自回归语言模型进行少样本命名实体识别（NER）；3）基于掩码语言模型结合现有与新语料进行NER；4）将工作流知识融入NER模型。基于新构建的52篇论文、16类实体标注的BioToFlow语料库，基于SciBERT的NER模型取得70.4的F-measure，与人工标注一致性相当。知识融合虽提升了特定实体性能，但在整体信息模式上效果有限。结果表明，高精度生物信息学工作流信息抽取在低资源条件下仍可实现。
原文摘要 · Abstract (English)
Bioinformatics workflows are essential for complex biological data analyses and are often described in scientific articles with source code in public repositories. Extracting detailed workflow information from articles can improve accessibility and reusability but is hindered by limited annotated corpora. To address this, we framed the problem as a low-resource extraction task and tested four strategies: 1) creating a tailored annotated corpus, 2) few-shot named-entity recognition (NER) with an autoregressive language model, 3) NER using masked language models with existing and new corpora, and 4) integrating workflow knowledge into NER models. Using BioToFlow, a new corpus of 52 articles annotated with 16 entities, a SciBERT-based NER model achieved a 70.4 F-measure, comparable to inter-annotator agreement. While knowledge integration improved performance for specific entities, it was less effective across the entire information schema. Our results demonstrate that high-performance information extraction for bioinformatics workflows is achievable.
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