Search (93 results, page 5 of 5)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × type_ss:"a"
  1. Zeng, M.L.; Gracy, K.F.; Zumer, M.: Using a semantic analysis tool to generate subject access points : a study using Panofsky's theory and two research samples (2014) 0.00
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  2. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.00
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    Source
    Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
  3. Greenberg, J.: Automatic query expansion via lexical-semantic relationships (2001) 0.00
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    Date
    29. 9.2001 13:59:48
  4. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.00
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    Date
    9.11.2008 13:07:29
  5. Bando, L.L.; Scholer, F.; Turpin, A.: Query-biased summary generation assisted by query expansion : temporality (2015) 0.00
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    Abstract
    Query-biased summaries help users to identify which items returned by a search system should be read in full. In this article, we study the generation of query-biased summaries as a sentence ranking approach, and methods to evaluate their effectiveness. Using sentence-level relevance assessments from the TREC Novelty track, we gauge the benefits of query expansion to minimize the vocabulary mismatch problem between informational requests and sentence ranking methods. Our results from an intrinsic evaluation show that query expansion significantly improves the selection of short relevant sentences (5-13 words) between 7% and 11%. However, query expansion does not lead to improvements for sentences of medium (14-20 words) and long (21-29 words) lengths. In a separate crowdsourcing study, we analyze whether a summary composed of sentences ranked using query expansion was preferred over summaries not assisted by query expansion, rather than assessing sentences individually. We found that participants chose summaries aided by query expansion around 60% of the time over summaries using an unexpanded query. We conclude that query expansion techniques can benefit the selection of sentences for the construction of query-biased summaries at the summary level rather than at the sentence ranking level.
  6. Gnoli, C.; Santis, R. de; Pusterla, L.: Commerce, see also Rhetoric : cross-discipline relationships as authority data for enhanced retrieval (2015) 0.00
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    Source
    Classification and authority control: expanding resource discovery: proceedings of the International UDC Seminar 2015, 29-30 October 2015, Lisbon, Portugal. Eds.: Slavic, A. u. M.I. Cordeiro
  7. Jiang, Y.; Bai, W.; Zhang, X.; Hu, J.: Wikipedia-based information content and semantic similarity computation (2017) 0.00
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    Date
    23. 1.2017 14:06:29
  8. Athukorala, K.; Glowacka, D.; Jacucci, G.; Oulasvirta, A.; Vreeken, J.: Is exploratory search different? : a comparison of information search behavior for exploratory and lookup tasks (2016) 0.00
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    Date
    18.10.2016 13:52:29
  9. Adhikari, A.; Dutta, B.; Dutta, A.; Mondal, D.; Singh, S.: ¬An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology (2018) 0.00
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    Date
    29. 7.2018 11:40:33
  10. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.00
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    Date
    22. 2.1996 13:14:10
  11. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.00
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    Date
    22. 3.2003 19:35:46
  12. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.00
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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]
  13. 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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