Search (51 results, page 3 of 3)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × type_ss:"a"
  1. Li, D.; Kwong, C.-P.; Lee, D.L.: Unified linear subspace approach to semantic analysis (2009) 0.01 
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  2. Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005) 0.01 
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  3. Bayer, O.; Höhfeld, S.; Josbächer, F.; Kimm, N.; Kradepohl, I.; Kwiatkowski, M.; Puschmann, C.; Sabbagh, M.; Werner, N.; Vollmer, U.: Evaluation of an ontology-based knowledge-management-system : a case study of Convera RetrievalWare 8.0 (2005) 0.01 
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  4. Tudhope, D.; Alani, H.; Jones, C.: Augmenting thesaurus relationships : possibilities for retrieval (2001) 0.01 
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  5. Pal, D.; Mitra, M.; Datta, K.: Improving query expansion using WordNet (2014) 0.01 
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    Abstract
    This study proposes a new way of using WordNet for query expansion (QE). We choose candidate expansion terms from a set of pseudo-relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand-crafted lexical resource such as WordNet. Experiments with a number of standard TREC collections WordNet-based that this method outperforms existing WordNet-based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well-known QE methods), we next propose a combination-based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (a) its distribution in the pseudo-relevant documents and in the target corpus, (b) its statistical association with query terms, and (c) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust (new), and TREC910 collections, and yields significant improvements over competing methods on most of these collections.
  6. Gnoli, C.; Santis, R. de; Pusterla, L.: Commerce, see also Rhetoric : cross-discipline relationships as authority data for enhanced retrieval (2015) 0.01 
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  7. Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018) 0.01 
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  8. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.01 
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    Source
    Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
  9. Brunetti, J.M.; Roberto García, R.: User-centered design and evaluation of overview components for semantic data exploration (2014) 0.01 
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    Date
    20. 1.2015 18:30:22
  10. Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: FACET: thesaurus retrieval with semantic term expansion (2002) 0.00 
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  11. Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014) 0.00 
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    Date
    20. 1.2015 18:30:22

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