Term weighting based class indexes using space density for Al-Qur'an relevant meaning ranking
| Autor: | Kurniawati, A'la Syauqi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 05 social sciences Search engine indexing 050301 education 02 engineering and technology computer.software_genre Class (biology) Term (time) Weighting Support vector machine Ranking Ranking SVM 0202 electrical engineering electronic engineering information engineering Vector space model 020201 artificial intelligence & image processing Data mining 0503 education computer |
| Zdroj: | 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS). |
| DOI: | 10.1109/icacsis.2016.7872753 |
| Popis: | Nowadays information retrieval based on specific queries is already used in computer system. One of the popular methods is document ranking using Vector Space Model (SVM) based on TF.IDF term-weighting. In this paper TF.IDF.ICS δ F term-weighting based class-indexing is proposed, afterward comparing its effectiveness to TF.IDF and TF.IDF.ICF term weighting. Each method is investigated through Al-Qur'an dataset. Al-Qur'an consist many verses, each verse of the Al-Qur'an is a single document which is ranked based on user query. The experimental show that the proposed method can be implemented on document ranking and the performance is better than previous methods with accurate value 93%. |
| Databáze: | OpenAIRE |
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