Name
Abstract | ||
|---|---|---|
Topic tracking, which starts from a few sample stories and finds all subsequent stories that discuss the same topic, is a new challenge for the text categorization task and is useful for timeline-based IR systems. Much previous research on topic tracking use machine learning techniques. However, the small size of the training data, especially positive training stories, presents difficulties in training the parameters of the topic tracking system to produce optimal results. In this paper, we present a method for topic tracking using subject templates and k-means clustering algorithm to select a suitable training set. The method was tested on the TDT1 corpus, and the result shows the effectiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.3115/1071884.1071896 | COLING |
Keywords | Field | DocType |
subject template,training data,optimal result,tdt1 corpus,positive training instance,positive training story,topic tracking use machine,previous research,new challenge,suitable training set,topic tracking system,topic tracking,tracking system,machine learning | Training set,Pattern recognition,Computer science,Tracking system,Timeline,Natural language processing,Artificial intelligence,Template,Cluster analysis,Text categorization,Machine learning | Conference |
Volume | Citations | PageRank |
C02-2 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yoshihiro sekiguchi | 1 | 0 | 0.34 |
| Fumiyo Fukumoto | 2 | 107 | 29.82 |
| Yoshihiro Sekiguchi | 3 | 29 | 6.86 |