arXiv:2412.15257
공식 코드 · Papers with Code
An Incremental Clustering Baseline for Event Detection on Twitter
원문 초록
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.
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Single-Document Summarization Using Sentence Embeddings and K-Means Clustering
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment
SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
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