TnT-LLM: Text Mining at Scale with Large Language Models-Reference-Cited by-同舟云学术

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TnT-LLM: Text Mining at Scale with Large Language Models

Published:2024-08-24 Issue: Volume:7 Page:5836-5847
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wan Mengting¹^ORCID,Safavi Tara¹^ORCID,Jauhar Sujay Kumar¹^ORCID,Kim Yujin¹^ORCID,Counts Scott¹^ORCID,Neville Jennifer¹^ORCID,Suri Siddharth¹^ORCID,Shah Chirag²^ORCID,White Ryen W.¹^ORCID,Yang Longqi¹^ORCID,Andersen Reid¹^ORCID,Buscher Georg¹^ORCID,Joshi Dhruv¹^ORCID,Rangan Nagu¹^ORCID

Affiliation:

1. Microsoft Corporation, Redmond, WA, USA

2. University of Washington, Seattle, WA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671647

Reference33 articles.

1. Charu C Aggarwal and ChengXiang Zhai. 2012. A survey of text clustering algorithms. Mining text data (2012), 77--128.

2. k-means: The advantages of careful seeding;Arthur David;Soda,2007

3. Léon Bottou. 1998. Online algorithms and stochastic approximations. Online learning in neural networks (1998).

4. An Intent Taxonomy for Questions Asked in Web Search

5. Jonathan Chang, Sean Gerrish, Chong Wang, Jordan Boyd-Graber, and David Blei. 2009. Reading tea leaves: How humans interpret topic models. Advances in neural information processing systems, Vol. 22 (2009).

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