使用基于NLP的文本分类来对作者关系进行地理 inference.
Brian Lee1, John S Brownstein2,3, Isaac S Kohane4
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. brian@kimlee.org.
Scientific reports
|October 16, 2024
概括
本研究引入了一种自然语言处理 (NLP) 模型,用于准确地从图书统计分析中提取作者位置. 该NLP模型克服了传统的地理分析工具的局限性,改进了研究网络分析.
科学领域:
- 图书统计学 图书统计学
- 计算语言学 计算语言学
- 地理信息学是一种地理信息学.
背景情况:
- 作者归属对于文献计量研究至关重要,需要准确的位置提取.
- 现有的地缘分析工具与非结构化关联扎,导致大规模分析中的错误和效率低下.
- 当前的机器学习地理解析器需要明确的位置数据,限制了它们的适用性.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 模型,用于从自由文本作者归属中预测城市,州和国家.
- 为了自动化位置推断,克服传统和现有的机器学习地理分析方法的局限性.
主要方法:
- 开发了一种使用文本分类技术的自然语言处理模型.
- 使用LinearSVC算法进行训练和预测.
- 使用MapAffil数据集和其他公共数据集验证模型.
主要成果:
- NLP模型准确地推断出高分辨率的作者位置,包括城市,州和国家.
- 与现有方法相比,在多个验证数据集中实现了更高的准确性.
- 证明有效的位置检索,即使在关联中缺少明确的地理数据.
结论:
- 开发的NLP模型提供了一个强大的解决方案,用于从作者归属中自动推断地理位置.
- 这一进步显著有利于图库计量研究,研究网络分析和理解全球研究分布.
- 突出了文本分类在提取特定的地理数据用于学术分析的实际应用.
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