Search (2 results, page 1 of 1)

  • × author_ss:"Huang, J.X."
  • × theme_ss:"Retrievalalgorithmen"
  1. Pan, M.; Huang, J.X.; He, T.; Mao, Z.; Ying, Z.; Tu, X.: ¬A simple kernel co-occurrence-based enhancement for pseudo-relevance feedback (2020) 0.00 
    0.0023983994 = product of:
  0.004796799 = sum of:
    0.004796799 = product of:
      0.009593598 = sum of:
        0.009593598 = weight(_text_:a in 5678) [ClassicSimilarity], result of:
          0.009593598 = score(doc=5678,freq=24.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.22065444 = fieldWeight in 5678, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5678)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Pseudo-relevance feedback is a well-studied query expansion technique in which it is assumed that the top-ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co-occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co-occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co-occurrence-based framework to enhance retrieval performance by integrating term co-occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co-occurrence-based Rocchio method (KRoc) and a kernel co-occurrence-based RM3 method (KRM3) are proposed. In our framework, co-occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within-document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state-of-the-art approaches.
    Type
    a
  2. Ye, Z.; Huang, J.X.: ¬A learning to rank approach for quality-aware pseudo-relevance feedback (2016) 0.00 
    0.0015481601 = product of:
  0.0030963202 = sum of:
    0.0030963202 = product of:
      0.0061926404 = sum of:
        0.0061926404 = weight(_text_:a in 2855) [ClassicSimilarity], result of:
          0.0061926404 = score(doc=2855,freq=10.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.14243183 = fieldWeight in 2855, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2855)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Pseudo relevance feedback (PRF) has shown to be effective in ad hoc information retrieval. In traditional PRF methods, top-ranked documents are all assumed to be relevant and therefore treated equally in the feedback process. However, the performance gain brought by each document is different as showed in our preliminary experiments. Thus, it is more reasonable to predict the performance gain brought by each candidate feedback document in the process of PRF. We define the quality level (QL) and then use this information to adjust the weights of feedback terms in these documents. Unlike previous work, we do not make any explicit relevance assumption and we go beyond just selecting "good" documents for PRF. We propose a quality-based PRF framework, in which two quality-based assumptions are introduced. Particularly, two different strategies, relevance-based QL (RelPRF) and improvement-based QL (ImpPRF) are presented to estimate the QL of each feedback document. Based on this, we select a set of heterogeneous document-level features and apply a learning approach to evaluate the QL of each feedback document. Extensive experiments on standard TREC (Text REtrieval Conference) test collections show that our proposed model performs robustly and outperforms strong baselines significantly.
    Type
    a

Authors

Years