Name
Title | ||
|---|---|---|
Evaluating the effectiveness of VSM model and topic segmentation in retrieving arabic documents. |
Abstract | ||
|---|---|---|
Information retrieval needs to match relevant texts with a given query. Selecting appropriate parts is useful when documents are long, and only portions are interesting to the user. In this paper, a set of IR experiments was carried out to study the impact of topic segmentation and its effect on Arabic information retrieval (IR). The system evaluation was conducted in two cases based on precision/recall criteria. Evaluate the system without using Arabic text segmentation and evaluate the system with Arabic text segmentation. Some famous information retrieval models, i.e., Vector Space Model, Relevance feedback Model were also adopted in our study for ranking relevant documents. Traditional data recall, precision and F1 measures were used to gauge IR effectiveness. A number of queries were selected and subjected to further detailed analysis to further explore the influence of topic segmentation on IR. The findings reveal that the system with topic segmentation gives better performance than the system without topic segmentation. |
Year | Venue | Keywords |
|---|---|---|
2011 | COMPUTER SYSTEMS SCIENCE AND ENGINEERING | Arabic language,Information Retrieval,Vector Space Model,Topic Segmentation,Text Mining |
Field | DocType | Volume |
Arabic,Segmentation,Computer science,Natural language processing,Artificial intelligence | Journal | 26 |
Issue | ISSN | Citations |
1 | 0267-6192 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fouzi Harrag | 1 | 27 | 3.75 |
| Aboubekeur Hamdi-Cherif | 2 | 4 | 1.40 |
| AbdulMalik S. Al-Salman | 3 | 141 | 18.35 |
| Eyas El-qawasmeh | 4 | 193 | 20.88 |