Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity

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Yousefi Shahin; Hooshmand Mohsen; Afsharchi Mohsen

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity

Author(s)

Yousefi Shahin | Hooshmand Mohsen | Afsharchi Mohsen

Keywords

Emerging Entity

Semantic Similarity

Online Clustering

Text Clustering

Early Discovery

Machine Learning

Natural Language Processing

Abstract

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 academy 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.

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APA: Copy

Yousefi, Shahin, Hooshmand, Mohsen, & Afsharchi, Mohsen. (). . . SID. https://sid.ir/paper/1046863/en

Vancouver: Copy

Yousefi Shahin, Hooshmand Mohsen, Afsharchi Mohsen. . . Available from: https://sid.ir/paper/1046863/en

IEEE: Copy

Shahin Yousefi, Mohsen Hooshmand, and Mohsen Afsharchi, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046863/en

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