원문정보
초록
영어
With a great deal of digitized textual information now available on the internet, it is almost impossible for people to assimilate all the information timely. Therefore, the technologies of topic detection and tracking are used for constructing news topics from news stories in order to bring convenience to people. However, traditional topic detection methods are not always so effective in detecting emerging hot news topics in a short period of time, and most topic detection methods use single-pass clustering algorithm which is with low accuracy and very sensitive to the input sequence of news documents. In order to improve clustering accuracy, we utilize a temporal distance factor to segment timeline into equal parts and propose a novel two-times single-pass clustering algorithm to deal with news stories in each part of timeline separately. Moreover, the aging theory is combined with our approach to build life-span model of topics from which we can obtain variation trend of hotness value of topics. The results of experiments show that our approach is effective and the life circle model of topics established by our method can conform to reality well.
목차
1. Introduction
2. Related Work
2.1. Topic Detection and Tracking
2.2. The Application of Aging Theory
3. Hot Topic Detection and Tracking
3.1. Preprocessing and Text Representation
3.2. A Two-Times Single-Pass Clustering Algorithm
3.3. The Definition of Aging Theory
3.4. Hot Topic Detection and Life-Span Modeling Algorithm
4. Experiments and Results Analysis
4.1. Dataset Description
4.2. Evaluation Metrics
4.3. The Selection of Time Distance
4.4. Topic Detection
4.5. Life-Span Modeling of Topics
5. Conclusions and Future Work
References