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  • × language_ss:"e"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × type_ss:"a"
  1. Vo, D.-T.; Bagheri, E.: Feature-enriched matrix factorization for relation extraction (2019) 0.00 
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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.
  2. Greenberg, J.: Automatic query expansion via lexical-semantic relationships (2001) 0.00 
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    Date
    29. 9.2001 13:59:48
  3. Tudhope, D.; Blocks, D.; Cunliffe, D.; Binding, C.: Query expansion via conceptual distance in thesaurus indexed collections (2006) 0.00 
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    Date
    30. 7.2011 16:07:29
  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. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.00 
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    Date
    22. 3.2003 19:35:46
  11. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.00 
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    Date
    22. 7.2006 16:32:43
  12. Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014) 0.00 
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    Date
    22. 8.2014 17:07:50
  13. Brezillon, P.; Saker, I.: Modeling context in information seeking (1999) 0.00 
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    Date
    21. 3.2002 19:29:27
  14. Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003) 0.00 
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    Source
    Bulletin of the American Society for Information Science. 29(2003) no.2, S.24-28
  15. Layfield, C.; Azzopardi, J,; Staff, C.: Experiments with document retrieval from small text collections using Latent Semantic Analysis or term similarity with query coordination and automatic relevance feedback (2017) 0.00 
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    Date
    10. 3.2017 13:29:57
  16. 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]
  17. Brunetti, J.M.; Roberto García, R.: User-centered design and evaluation of overview components for semantic data exploration (2014) 0.00 
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    Date
    20. 1.2015 18:30:22
  18. 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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