UNIBA-SENSE at CLEF 2008: SEmantic N-levels Search Engine

This paper presents evaluation experiments conducted at the University of Bari for the Ad-Hoc Robust WSD task of the Cross-Language Evaluation Forum (CLEF) 2008. The evaluation was performed using SENSE (SEmantic N-levels Search Engine) [2]. SENSE tries to overcome the limitations of the ranked keyword approach by introducing semantic levels, which integrate (and not simply replace) the lexical level represented by keywords. We show how SENSE is able to manage documents indexed at two separate levels, keywords and word meanings, as well as to combine keyword search with semantic information provided by the other indexing levels, in an attempt of improving the retrieval performance. Two types of experiments have been performed, by exploiting only one indexing level and exploiting all indexing levels at the same time. The experiments performed combining keywords and word meanings, extracted from the WordNet lexical database, show the promise of the idea and point out the value of our intuition. In particular the results confirm our hypothesis that the combination of two indexing levels outperforms a single level. Indeed, an improvement of 35% in precision whose obtained by adopting the N-levels model with respect to the results obtained by exploiting the indexing level based only on keywords. Moreover, the Precision-Recall curve shows that the N-levels model outperforms the keywords level at all values of recall.