Detecting topic drift

Knights, Dan; Mozer, Mike; Nicolov, Nicolas

doi:10.1075/cilt.309.09kni

In:Recent Advances in Natural Language Processing V: Selected papers from RANLP 2007
Edited by Nicolas Nicolov, Galia Angelova and Ruslan Mitkov
[Current Issues in Linguistic Theory 309] 2009
► pp. 113–130

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Detecting topic drift

Dan Knights

Mike Mozer

Nicolas Nicolov

Published online: 22 October 2009

https://doi.org/10.1075/cilt.309.09kni

Cited by (7)

Cited by seven other publications

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Wang, Chengyu, Xiaofeng He & Aoying Zhou

2018. Event phase oriented news summarization. World Wide Web 21:4 ► pp. 1069 ff.

Mukherjee, Sanjoy Kumar & Sivaji Bandyopadhyay

2017. Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach. Journal of Big Data 4:1

Topal, Kamil, Mehmet Koyutürk & Gültekin Özsoyoğlu

2017. Effects of emotion and topic area on topic shifts in social media discussions. Social Network Analysis and Mining 7:1

Wang, Chengyu, Rong Zhang, Xiaofeng He, Guomin Zhou & Aoying Zhou

2016. Event Phase Extraction and Summarization. In Web Information Systems Engineering – WISE 2016 [Lecture Notes in Computer Science, 10041], ► pp. 473 ff.

Arnold, Taylor & Lauren Tilton

2015. Text Analysis. In Humanities Data in R [Quantitative Methods in the Humanities and Social Sciences, ], ► pp. 157 ff.

Chen, Yan, Xiaoming Zhang, Zhoujun Li & Jun-Ping Ng

2015. Search engine reinforced semi-supervised classification and graph-based summarization of microblogs. Neurocomputing 152 ► pp. 274 ff.

Jameel, Shoaib & Wai Lam

2013. An N-Gram Topic Model for Time-Stamped Documents. In Advances in Information Retrieval [Lecture Notes in Computer Science, 7814], ► pp. 292 ff.

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