Obtaining More Specific Topics and Detecting Weak Signals by Topic Word Selection

Author:

Kölbl Laura,Grottke Michael

Publisher

Springer International Publishing

Reference17 articles.

1. Chang J, Gerrish S, Wang C, Boyd-Graber J, Blei DM (2009) Reading tea leaves: how humans interpret topic models. In: Advances in neural information processing systems, vol 22. Vancouver, Canada, pp 288–296

2. Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on web search and data mining. Shanghai, China, pp 399–408

3. Blei D, Ng A, Jordan M (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

4. Lau, JH, Newman D, Karimi S, Baldwin T (2010) Best Topic Word Selection for Topic Labelling. In: Proceedings of the 23rd international conference on computational linguistics. Beijing, China, pp 605–613

5. He D, Wang M, Khattak AM, Zhang L, Gao W (2019) Automatic labeling of topic models using graph-based ranking. IEEE Access 7:131593–131608

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2. Review on adopting concept extraction in weak signals detection in competitive intelligence;ArabWIC 2021: The 7th Annual International Conference on Arab Women in Computing in Conjunction with the 2nd Forum of Women in Research, Sharjah, UAE;2021-08-25

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