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Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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Wu, H.; He, J.; Pei, Y.: Scientific impact at the topic level : a case study in computational linguistics (2010)
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Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012)
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Kim, S.; Ko, Y.; Oard, D.W.: Combining lexical and statistical translation evidence for cross-language information retrieval (2015)
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Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015)
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Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012)
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AL-Smadi, M.; Jaradat, Z.; AL-Ayyoub, M.; Jararweh, Y.: Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features (2017)
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Luo, Z.; Yu, Y.; Osborne, M.; Wang, T.: Structuring tweets for improving Twitter search (2015)
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Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014)
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Doval, Y.; Gómez-Rodríguez, C.: Comparing neural- and N-gram-based language models for word segmentation (2019)
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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
-
Kajanan, S.; Bao, Y.; Datta, A.; VanderMeer, D.; Dutta, K.: Efficient automatic search query formulation using phrase-level analysis (2014)
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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
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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
-
Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018)
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- Date
- 22. 1.2018 11:32:44
-
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
