一种深度语义匹配方法,用于识别社交媒体分析的相关消息
Frederick Brown Biggers1, Somya D Mohanty2, Prashanti Manda3
1Artificial Intelligence and Natural Language Processing, United Health Group, Raleigh, NC, USA.
Scientific reports
|July 25, 2023
概括
本研究介绍了一种使用Word2Vec的方法,用于跟踪危机推文中的词语含义的变化,改进与事件相关的数据检索的自然语言处理.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
背景情况:
- 社交媒体内容对NLP应用越来越有价值.
- 鉴定特定事件的相关推文是计算上具有挑战性的,因为复杂的语义和各种语言的使用.
- 现有的方法难以捕捉语言在事件发生过程中的动态性质.
研究的目的:
- 用风伊尔玛作为案例研究,展示分析危机事件期间Twitter语义变化的方法.
- 开发一个基于动态的,与上下文相关的相关性评分推文的机制.
- 为了证明这个词的相似性不是静态的,可以模拟.
主要方法:
- 实现Word2Vec神经网络模型以创建词嵌入.
- 训练Word2Vec矢量模型使用各种方法来识别相关的推文.
- 探索参数调整 (词窗口大小,最小词频率,隐藏层维度,负采样) 以实现模型性能.
主要成果:
- 展示了在危机事件中词语的相对含义如何演变.
- 介绍了一个基于动态,相对上下文相关性的推特评分机制.
- 确定了Word2Vec模型性能的最佳参数设置,由AU_ROC表示.
结论:
- 拟议的方法有效地识别了相关的tweet corpora,用于危机事件分析.
- 词嵌入捕捉了社交媒体语言中的动态语义转变.
- 参数调整对于优化社交媒体数据上的NLP模型以进行事件分析至关重要.
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