动物疾病监测:如何表示文本数据来分类流行病学信息
Sarah Valentin1, Rémy Decoupes2, Renaud Lancelot3
1CIRAD, F-34398 Montpellier, France; ASTRE, Univ Montpellier, CIRAD, INRAE, Montpellier, France; TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France; Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec, Canada.
Preventive veterinary medicine
|May 29, 2023
概括
机器学习方法有效地分类动物疾病新闻. 词嵌入擅长识别特定的流行病学主题,改善早期疾病检测和准备.
科学领域:
- 兽医流行病学 兽医流行病学
- 计算语言学计算语言学
- 机器学习是机器学习.
背景情况:
- 像新闻这样的非正式来源对于及时检测疾病爆发非常有价值.
- 早期预警系统从详细的流行病学信息中受益.
- 分类动物疾病新闻需要强大的自然语言处理方法.
研究的目的:
- 评估机器学习方法来分类动物疾病新闻.
- 为了比较词包和词嵌入文本表示.
- 评估细粒度流行病学话题分类中的表现.
主要方法:
- 利用机器学习来对与动物疾病相关的新闻进行文本分类.
- 词包和词嵌入 (分布式) 文本表示的比较.
- 使用F-测量对二进制和细粒度任务进行评估的分类性能.
主要成果:
- 词包和词嵌入在二进制相关性分类中表现良好.
- 词嵌入在细粒度的流行病学话题分类中显著超过了词袋 (F-措施0.745对比0.745).
- 词嵌入显示为低频类在专业领域的承诺.
结论:
- 词嵌入对于在动物疾病监测等专业领域的细粒度主题分类是有益的.
- 文本表示的选择应根据具体的分类任务量身定制.
- 机器学习增强了用于疾病准备的非正式数据的分析.
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