实体对公众意见的两步匹配框架的看法
Ren-De Li1, Hao-Tian Ma2, Zi-Yi Wang3
1Library and Research Center of Computer Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
概括
本研究引入了一种两步匹配方法,用于在公众评论中准确识别目标实体. 该方法显著提高了对公众论分析的实体认可准确性.
科学领域:
- 自然语言处理自然语言处理.
- 信息检索 信息检索
- 计算语言学 计算语言学
背景情况:
- 在模糊的用户评论中准确识别目标实体对于分析公众意见至关重要.
- 现有的方法与公共话语中提到的多个实体进行斗争,阻碍了精确的目标识别.
研究的目的:
- 提出和评估一种新的两步匹配方法,以从公众评论中准确识别目标实体.
- 提高在大量公众论数据中识别特定实体的准确性.
主要方法:
- 使用BiLSTM-CRF模型用于潜在实体提取和TF-IDF用于特征词提取.
- 实施了两步匹配过程,涉及Jaro-Winkler距离与业务目录和行业特征词典相匹配.
- 定义关联率和准确率作为匹配准确性的关键绩效指标.
主要成果:
- 两步匹配方法在三个公共卫生事件数据集上实现了高关联率 (高达0.93) 和准确率 (高达0.95).
- 与仅使用初始匹配步骤相比,在关联率和准确率上平均提高了32%,精度提高了30%.
- 产业特征词典显著提高了实体识别的精度.
结论:
- 拟议的两步匹配框架有效地从模两可的公众评论中确定精确的目标实体.
- 这种方法为从大规模的论数据中提取特定信息提供了强大的解决方案.
- 该框架对公共卫生监督和市场研究等领域的目标识别有重大影响.
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