A Scale-Space Theory for Text1st submitted: Jan. 15 2012; revised: Mar. 28 2012.
Table 2: Text retrieval performance. We evaluate four models: the Unigram Language Model (1-LM) and the Scale-Invariant Language Models with three textual signal options (referred to as SILM.BOW, SILM.LDA and SILM.Sentence respectively).
['Model∖Measure', 'MAP', 'P@5', 'P@10']
We evaluated SILM on a text retrieval task based on the OHSUMED data set, a collection of 348,566 documents, 106 queries and 16,140 relevance judgements. For SILM, standard Kullback-Leibler divergence was used as relevance function. For comparison, the unigram language model (i.e., 1-LM) was used as baselines. We observe that SILM models outperform the uni-gram LM amazingly by (up to) 15% in terms of MAP, 13% in P@5 and 10% in P@10. All these improvements are significant based on a Wilcoxon test at the level of 0.01. Again, the best performance is obtained by the sentence-level 2D based SILM model.