14 citations · 43 across the 14 of their papers we have counts for
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cs.CL2024
Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research
Yida Mu, Mali Jin, Xingyi Song +1
Research in natural language processing (NLP) for Computational Social Science (CSS) heavily relies on data from social media platforms. This data plays a crucial role in the devel…
cs.CL2024★ 5 cited
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Yida Mu, Peizhen Bai, Kalina Bontcheva +1
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification app…
cs.CL2024★ 14 cited
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Yida Mu, Chun Dong, Kalina Bontcheva +1
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic…