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Exploring the Space of Topic Coherence Measures
Abstract
Quantifying the coherence of a set of statements is a long standing problem with many potential applications that has attracted researchers from different sciences. The special case of measuring coherence of topics has been recently studied to remedy the problem that topic models give no guaranty on the interpretablity of their output. Several benchmark datasets were produced that record human judgements of the interpretability of topics. We are the first to propose a framework that allows to construct existing word based coherence measures as well as new ones by combining elementary components. We conduct a systematic search of the space of coherence measures using all publicly available topic relevance data for the evaluation. Our results show that new combinations of components outperform existing measures with respect to correlation to human ratings. nFinally, we outline how our results can be transferred to further applications in the context of text mining, information retrieval and the world wide web.
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Cited by
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Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020) · Journal of Medical Internet Research · doi:10.2196/20550 · Cited by 369
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Bianchi, F., Terragni, S., & Hovy, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.18653/v1/2021.acl-short.96 · Cited by 269
A Systematic Review of Generative AI for Teaching and Learning Practice
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024) · Education Sciences · doi:10.3390/educsci14060636 · Cited by 251
Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)
Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., & Islam, M. J. (2021) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-33-4673-4_27 · Cited by 119
Investigating Code Generation Performance of ChatGPT with Crowdsourcing Social Data
Feng, Y., Vanam, S., Cherukupally, M., Zheng, W., Qiu, M., & Chen, H. (2023) · 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00117 · Cited by 117
Metadata
- Authors
- Röder, M., Both, A., & Hinneburg, A.
- Venue
- Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2015-02-02
- Pages
- 399-408
- OpenAlex
- W2038043464
- Cited by
- 2,224
- References
- 0
- FWCI
- 23.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the Space of Topic Coherence Measures. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/2684822.2685324
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