Abstract
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task - discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.
| Original language | English (US) |
|---|---|
| Pages | 198-207 |
| Number of pages | 10 |
| State | Published - 2005 |
| Event | KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States Duration: Aug 21 2005 → Aug 24 2005 |
Other
| Other | KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|---|
| Country/Territory | United States |
| City | Chicago, IL |
| Period | 8/21/05 → 8/24/05 |
Keywords
- Clustering
- Evolutionary theme patterns
- Temporal text mining
- Theme threads
ASJC Scopus subject areas
- Software
- Information Systems