提高模型预测在文档级别命名实体识别上的一致性
Minbyul Jeong1, Jaewoo Kang1,2,3
1Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
Bioinformatics (Oxford, England)
|June 1, 2023
概括
本研究介绍了ConNER,这是一种通过改进修饰剂标签来改进生物医学命名实体识别 (NER) 的新方法. 康纳提高了标签的一致性,提高了对具有挑战性的数据集的性能.
科学领域:
- 生物医学自然语言处理
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 生物医学命名实体识别 (NER) 对于生物医学应用中的信息提取至关重要.
- 文档级别的NER提出了超越句子级别分析的挑战,往往导致不一致的预测.
- 现有的NER文件模型难以满足研究人员对标签一致性的期望.
研究的目的:
- 调查文档级生物医学NER中不一致预测的原因.
- 开发一种方法,ConNER,以提高标签的一致性,特别是对形容词和前置词等修饰符.
- 通过改进修饰剂标签来改善生物医学实体的表现.
主要方法:
- 研究生物医学NER中不一致预测的潜在原因.
- 开发了ConNER,这是一种在实体内对变量 (形容词,前置词) 的标签进行精细化的方法.
- 在四个流行的生物医学NER数据集上评估了ConNER.
主要成果:
- 在四个数据集中,康纳在三个数据集中获得了比最先进的F1得分更高的F1得分.
- 在两个数据集上显示了7.5%-8.6%的绝对F1得分改善.
- 定性分析证实了ConNER能够产生更一致的NER预测的能力.
结论:
- 康纳有效地提高了生物医学NER中的标签一致性,特别是对于内在一致性较低的数据集.
- 该方法通过改进修饰剂标签来改善生物医学实体的表示.
- 康纳提供了一个有前途的方法,可以在生物医学文本中提取更可靠的信息.
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