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Korman, D.Z.; Mack, E.; Jett, J.; Renear, A.H.: Defining textual entailment (2018)
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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.
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Becks, D.; Schulz, J.M.: Domänenübergreifende Phrasenextraktion mithilfe einer lexikonunabhängigen Analysekomponente (2010)
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- Source
- Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
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Sünkler, S.; Kerkmann, F.; Schultheiß, S.: Ok Google . the end of search as we know it : sprachgesteuerte Websuche im Test (2018)
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- Content
- Vgl.: https://www.b-i-t-online.de/heft/2018-01-index.php.
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Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013)
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- Date
- 22. 3.2015 9:30:24
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Perovsek, M.; Kranjca, J.; Erjaveca, T.; Cestnika, B.; Lavraca, N.: TextFlows : a visual programming platform for text mining and natural language processing (2016)
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Mengel, T.: Wie viel Terminologiearbeit steckt in der Übersetzung der Dewey-Dezimalklassifikation? (2019)
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-
Multi-source, multilingual information extraction and summarization (2013)
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- Editor
- Poibeau, T. u.a.
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Ye, Z.; He, B.; Wang, L.; Luo, T.: Utilizing term proximity for blog post retrieval (2013)
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-
Luo, Z.; Yu, Y.; Osborne, M.; Wang, T.: Structuring tweets for improving Twitter search (2015)
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-
Brychcín, T.; Konopík, M.: HPS: High precision stemmer (2015)
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-
Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012)
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Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016)
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-
Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017)
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Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012)
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- Date
- 10. 1.2013 19:22:47
-
Lawrie, D.; Mayfield, J.; McNamee, P.; Oard, P.W.: Cross-language person-entity linking from 20 languages (2015)
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- Abstract
- The goal of entity linking is to associate references to an entity that is found in unstructured natural language content to an authoritative inventory of known entities. This article describes the construction of 6 test collections for cross-language person-entity linking that together span 22 languages. Fully automated components were used together with 2 crowdsourced validation stages to affordably generate ground-truth annotations with an accuracy comparable to that of a completely manual process. The resulting test collections each contain between 642 (Arabic) and 2,361 (Romanian) person references in non-English texts for which the correct resolution in English Wikipedia is known, plus a similar number of references for which no correct resolution into English Wikipedia is believed to exist. Fully automated cross-language person-name linking experiments with 20 non-English languages yielded a resolution accuracy of between 0.84 (Serbian) and 0.98 (Romanian), which compares favorably with previously reported cross-language entity linking results for Spanish.
-
Fóris, A.: Network theory and terminology (2013)
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- Date
- 2. 9.2014 21:22:48
-
Nagy T., I.: Detecting multiword expressions and named entities in natural language texts (2014)
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Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018)
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- Date
- 22. 1.2018 11:32:44
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Deventer, J.P. van; Kruger, C.J.; Johnson, R.D.: Delineating knowledge management through lexical analysis : a retrospective (2015)
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- Date
- 20. 1.2015 18:30:22
