用增量聚类+新句向量，提升推特事件检测效率
An Incremental Clustering Baseline for Event Detection on Twitter
- 增量聚类结合最新句向量技术
- 相比已有研究性能显著提升
- 适合关注社交媒体事件检测的研究者
文本流中的事件检测是在线媒体与社交网络分析的关键任务。当前该领域的一个挑战是在保持可接受计算复杂度的前提下建立性能基准。本研究采用增量聚类算法，并结合近期句子嵌入技术的进展。目标是将结果与Cao等（2024）和Mazoyer等（2020）的研究进行比较。结果表明，该方法实现了显著性能提升，可作为未来该方向研究的可靠基线。
原文摘要 · Abstract (English)
Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。