Name
Abstract | ||
|---|---|---|
In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-15754-7_19 | CLEF (1) |
Keywords | Field | DocType |
wordnet relation,existing information retrieval system,information retrieval task,basic level concepts,better recall,semantic classes approach,wordnet term weighting approach,clef robust-wsd task,wordnet domain,semantic class,different ontology,new method,information retrieval system,information retrieval | Ontology (information science),Weighting,Information retrieval,Computer science,Precision and recall,Natural language processing,Artificial intelligence,Word sense,WordNet,Concept search,Word-sense disambiguation,Clef | Conference |
Volume | ISSN | ISBN |
6241 | 0302-9743 | 3-642-15753-X |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javi Fernandez | 1 | 20 | 5.65 |
| Rubén Izquierdo | 2 | 96 | 14.14 |
| José M. Gómez | 3 | 32 | 8.02 |