Name
Abstract | ||
|---|---|---|
This paper presents a domain-independent approach for partitioning text documents into a set of topic-coherent segment units, where the structure of segments reflects the patterns of sub-topics of the processed text document. The approach adopts similarity analyses, which is based on Shannon Information Theory, to determine topic distribution among text documents without incorporating thesaurus information and other auxiliary knowledge bases. It first observes the documents in terms of consistency of distribution from the viewpoint of individual word and then constructs a number of segmentation proposals accordingly. Furthermore, it employs the K-means clustering technique to get a consensus from these proposals and finally partition text into a set of topic coherent paragraphs. Through extensive experimental studies based on real and synthetic data sources, the performance analysis illustrates the effectiveness of the approach in text segmentation. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11892960_51 | KES (1) |
Keywords | Field | DocType |
segmentation proposal,text segmentation,text document,partitioning text document,efficient text segmentation,k-means clustering technique,topic coherent paragraph,partition text,processed text document,automatic approach,topic distribution,domain-independent approach,synthetic data,k means clustering,information theory,knowledge base,k means,shannon information theory | Information theory,k-means clustering,Data mining,Information retrieval,Computer science,Segmentation,Text segmentation,Synthetic data,Knowledge engineering,Case-based reasoning,Cluster analysis,Distributed computing | Conference |
Volume | ISSN | ISBN |
4251 | 0302-9743 | 3-540-46535-9 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keke Cai | 1 | 243 | 15.36 |
| Jiajun Bu | 2 | 4106 | 211.52 |
| Chun Chen | 3 | 4727 | 246.28 |
| Peng Huang | 4 | 4 | 2.77 |