Name
Abstract | ||
|---|---|---|
In this study, two new document weighting methods are proposed based on term frequency-inverse document frequency (TF-IDF) generally used in text mining methods. Also, insignificance of the verb in text classification which will be a new method in pre-processing have been put forward and tested. The better results were observed through using these methods when these methods compare with other method, It was observed that the performance rate hardly change and the data size which was processed decreased by omitting verbs of texts. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SIU.2015.7129862 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
inverse document frequency,term weighting,text classification | Text graph,Verb,Weighting,Text mining,Pattern recognition,Computer science,Feature extraction,Performance rate,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erdal Kiliç | 1 | 2 | 1.13 |
| Ates, Nurullah | 2 | 0 | 0.34 |
| Karakaya, Aykut | 3 | 0 | 0.34 |
| Sahin, Durmus Ozkan | 4 | 0 | 1.35 |