Search (39 results, page 1 of 2)

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  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Zarri, G.P.: Utilisation de structures conceptuelles complexes pour représenter le "contenu sémantique" de textes narratifs (1999) 0.02
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    Language
    f
  2. Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011) 0.02
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  3. Shapiro, C.D.; Yan, P.-F.: Generous tools : thesauri in digital libraries (1996) 0.02
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  4. Colace, F.; Santo, M. De; Greco, L.; Napoletano, P.: Weighted word pairs for query expansion (2015) 0.02
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  5. Scholer, F.; Williams, H.E.; Turpin, A.: Query association surrogates for Web search (2004) 0.02
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  6. Menczer, F.: Lexical and semantic clustering by Web links (2004) 0.02
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  7. Mayr, P.; Schaer, P.; Mutschke, P.: ¬A science model driven retrieval prototype (2011) 0.02
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    Source
    Concepts in context: Proceedings of the Cologne Conference on Interoperability and Semantics in Knowledge Organization July 19th - 20th, 2010. Eds.: F. Boteram, W. Gödert u. J. Hubrich
  8. Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015) 0.02
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  9. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.01
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  10. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01
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    Date
    30. 3.2001 13:32:22
  11. 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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  12. Boteram, F.: Typisierung semantischer Relationen in integrierten Systemen der Wissensorganisation (2013) 0.01
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  13. Bando, L.L.; Scholer, F.; Turpin, A.: Query-biased summary generation assisted by query expansion : temporality (2015) 0.01
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  14. Buccio, E. Di; Melucci, M.; Moro, F.: Detecting verbose queries and improving information retrieval (2014) 0.01
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  15. Cai, F.; Rijke, M. de: Learning from homologous queries and semantically related terms for query auto completion (2016) 0.01
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  16. Vo, D.-T.; Bagheri, E.: Feature-enriched matrix factorization for relation extraction (2019) 0.01
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    Abstract
    Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.
  17. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.01
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    Date
    1. 2.2016 18:25:22
  18. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.01
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    Date
    1. 2.2016 18:25:22
  19. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.01
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  20. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.01
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

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  • f 1
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