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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kuan-Yu Chen Luesukprasert, L. Chou, S.-c.T. |
| Copyright Year | 1989 |
| Abstract | With the vast amount of digitized textual materials now available on the Internet, it is almost impossible for people to absorb all pertinent information in a timely manner. To alleviate the problem, we present a novel approach for extracting hot topics from disparate sets of textual documents published in a given time period. Our technique consists of two steps. First, hot terms are extracted by mapping their distribution over time. Second, based on the extracted hot terms, key sentences are identified and then grouped into clusters that represent hot topics by using multidimensional sentence vectors. The results of our empirical tests show that this approach is more effective in identifying hot topics than existing methods. |
| Sponsorship | IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society |
| File Size | 2823017 |
| File Format | |
| ISSN | 10414347 |
| Volume Number | 19 |
| Issue Number | 8 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Multidimensional systems Data mining Event detection Information analysis Internet Testing Explosions Humans Organizing Aggregates topic detection and tracking. Aging theory clustering hot topic detection term weighting |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems Computational Theory and Mathematics Computer Science Applications |
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