While keyphrase extraction has received considerable attention in recent
years, relatively few studies exist on extracting keyphrases from social media
platforms such as Twitter, and even fewer for extracting disaster-related
keyphrases from such sources. During a disaster, keyphrases can be extremely
useful for filtering relevant tweets that can enhance situational awareness.
Previously, joint training of two different layers of a stacked Recurrent
Neural Network for keyword discovery and keyphrase extraction had been shown to
be effective in extracting keyphrases from general Twitter data. We improve the
model's performance on both general Twitter data and disaster-related Twitter
data by incorporating contextual word embeddings, POS-tags, phonetics, and
phonological features. Moreover, we discuss the shortcomings of the often used
F1-measure for evaluating the quality of predicted keyphrases with respect to
the ground truth annotations. Instead of the F1-measure, we propose the use of
embedding-based metrics to better capture the correctness of the predicted
keyphrases. In addition, we also present a novel extension of an
embedding-based metric. The extension allows one to better control the penalty
for the difference in the number of ground-truth and predicted keyphrases

本文探讨如何从社交媒体平台（如 Twitter）中提取与灾难有关的关键词，以帮助加强对灾害现场情况的认识。作者提出了一种基于嵌入式度量的模型来评估关键词提取的质量，这一模型比 F1 得分更准确。作者改进了嵌入式度量模型，并通过使用上下文词嵌入、POS 标签、音标和音韵学特征来提高模型在 Twitter 通用数据和相关数据上的性能。