Search (77 results, page 3 of 4)

  • × language_ss:"e"
  • × theme_ss:"Computerlinguistik"
  1. Schmolz, H.: Anaphora resolution and text retrieval : a lnguistic analysis of hypertexts (2013) 0.01 
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  2. Snajder, J.: Distributional semantics of multi-word expressions (2013) 0.01 
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    Content
    Folien einer Präsentation anlässlich COST Action IC1207 PARSEME Meeting, Warsaw, September 16, 2013. Vgl. den Beitrag: Snajder, J., P. Almic: Modeling semantic compositionality of Croatian multiword expressions. In: Informatica. 39(2015) H.3, S.301-309.
  3. Fóris, A.: Network theory and terminology (2013) 0.01 
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    Date
    2. 9.2014 21:22:48
  4. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.01 
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    Date
    23.11.2023 19:07:22
  5. Rahmstorf, G.: Information retrieval using conceptual representations of phrases (1994) 0.01 
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    Source
    Information systems and data analysis: prospects - foundations - applications. Proc. of the 17th Annual Conference of the Gesellschaft für Klassifikation, Kaiserslautern, March 3-5, 1993. Ed.: H.-H. Bock et al
  6. Ingenerf, J.: Disambiguating lexical meaning : conceptual meta-modelling as a means of controlling semantic language analysis (1994) 0.01 
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    Source
    Information systems and data analysis: prospects - foundations - applications. Proc. of the 17th Annual Conference of the Gesellschaft für Klassifikation, Kaiserslautern, March 3-5, 1993. Ed.: H.-H. Bock et al
  7. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.01 
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  8. Korman, D.Z.; Mack, E.; Jett, J.; Renear, A.H.: Defining textual entailment (2018) 0.01 
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    Abstract
    Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H?=?df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.
  9. Moisl, H.: Artificial neural networks and Natural Language Processing (2009) 0.01 
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  10. Perera, P.; Witte, R.: ¬A self-learning context-aware lemmatizer for German (2005) 0.01 
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    Content
    Vgl. unter: http://acl.ldc.upenn.edu//H/H05/H05-1080.pdf.
  11. Schmid, H.: Improvements in Part-of-Speech tagging with an application to German (1995) 0.01 
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  12. Keselman, A.; Rosemblat, G.; Kilicoglu, H.; Fiszman, M.; Jin, H.; Shin, D.; Rindflesch, T.C.: Adapting semantic natural language processing technology to address information overload in influenza epidemic management (2010) 0.01 
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  13. Agarwal, B.; Ramampiaro, H.; Langseth, H.; Ruocco, M.: ¬A deep network model for paraphrase detection in short text messages (2018) 0.01 
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  14. Lee, Y.-H.; Evens, M.W.: Natural language interface for an expert system (1998) 0.01 
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  15. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.01 
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    Source
    Advances in classification research, vol.10: proceedings of the 10th ASIS SIG/CR Classification Research Workshop. Ed.: Albrechtsen, H. u. J.E. Mai
  16. Chen, K.-H.: Evaluating Chinese text retrieval with multilingual queries (2002) 0.01 
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  17. Kuo, J.-S.; Li, H.; Yang, Y.-K.: Active learning for constructing transliteration lexicons from the Web (2008) 0.01 
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  18. Wu, H.; He, J.; Pei, Y.: Scientific impact at the topic level : a case study in computational linguistics (2010) 0.01 
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  19. Radev, D.R.; Joseph, M.T.; Gibson, B.; Muthukrishnan, P.: ¬A bibliometric and network analysis of the field of computational linguistics (2016) 0.01 
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    Abstract
    The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, PageRank, metrics across publication years and venues, the impact factor and h-index, as well as other measures.
  20. Deventer, J.P. van; Kruger, C.J.; Johnson, R.D.: Delineating knowledge management through lexical analysis : a retrospective (2015) 0.01 
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    Date
    20. 1.2015 18:30:22

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