Name
Abstract | ||
|---|---|---|
This paper describes two different approaches for incorporating background knowledge into nearest-neighbor text classification. Our first approach uses background text to assess the similarity between training and test documents rather than assessing their similarity directly. The second method redescribes examples using Latent Semantic Indexing on the background knowledge, assessing document similarities in this redescribed space. Our experimental results showthat both approaches can improve the performance of nearest-neighbor text classification. These methods are especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a specific problem on the World Wide Web. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-46119-1_1 | ECCBR |
Keywords | Field | DocType |
nearest-neighbor text classification,latent semantic indexing,labor-intensive job,integrating background knowledge,large amount,different approach,document similarity,world wide web,background text,nearest neighbor | k-nearest neighbors algorithm,Nearest neighbour,Knowledge representation and reasoning,Latent semantic indexing,Indexation,Information retrieval,Computer science,Search engine indexing,Latent semantic analysis | Conference |
Volume | ISSN | ISBN |
2416 | 0302-9743 | 3-540-44109-3 |
Citations | PageRank | References |
18 | 1.02 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Zelikovitz | 1 | 181 | 16.42 |
| Haym Hirsh | 2 | 1839 | 277.74 |