- (Wang & McCallum, 2006) ⇒ Xuerui Wang, and Andrew McCallum. (2006). “Topics Over Time: a non-Markov continuous-time model of topical trends.” In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006). doi:10.1145/1150402.1150450
Subject Headings: Topic Tracking Task.
- ~199 http://scholar.google.com/scholar?q=%22Topics+Over+Time%3A+a+non-Markov+continuous-time+model+of+topical+trends%22+2006
This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps, and for each generated document, the mixture distribution over topics is influenced by both word co-occurrences and the document's timestamp. Thus, the meaning of a particular topic can be relied upon as constant, but the topics' occurrence and correlations change significantly over time. We present results on nine months of personal email, 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses, showing improved topics, better timestamp prediction, and interpretable trends.
|2006 TopicsOverTime||Xuerui Wang|
|Topics Over Time: a non-Markov continuous-time model of topical trends||KDD-2006 Proceedings||http://www.cs.umass.edu/~mccallum/papers/tot-kdd06.pdf||10.1145/1150402.1150450||2006|
|Author||Xuerui Wang + and Andrew McCallum +|
|journal||Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Topics Over Time: a non-Markov continuous-time model of topical trends +|