利用语义相似度早期发现波斯语 Twitter 中的新兴实体
Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity
AI总结：
针对波斯语 Twitter 新兴实体早期发现中的数据与评估难题，本文提出无需训练的在线聚类方法 EEPT，并用新指标验证其可提前发现重要实体。
摘要：
发现新兴实体（emerging entities，EEs）是指在实体确立之前找到它们的问题。这些实体对个人、公司和政府都可能至关重要。许多此类实体可以在社交媒体平台（例如 Twitter）上被发现。近年来，这些身份一直是学术界和工业界的研究热点。与任何机器学习问题类似，数据可用性是该问题中的主要挑战之一。本文提出了 EEPT。这是一种在线聚类方法，能够发现新兴实体，而无需在数据集上进行任何训练。此外，由于缺乏合适的评估指标，本文使用一种新的指标来评估结果。结果表明，EEPT 具有良好前景，并能在重要实体确立之前发现它们。
英文摘要：
Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academia and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT. That is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
