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Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.08

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Source: c't. 2000, H.22, S.230-231

Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.07

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Content: Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
Date: 8. 1.2013 10:22:32

Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.06

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Source: c't. 2000, H.22, S.220-229

Schneider, J.W.; Borlund, P.: ¬A bibliometric-based semiautomatic approach to identification of candidate thesaurus terms : parsing and filtering of noun phrases from citation contexts (2005) 0.04

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Date: 8. 3.2007 19:55:22

Drouin, P.: Term extraction using non-technical corpora as a point of leverage (2003) 0.03

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Ekmekcioglu, F.C.; Willett, P.: Effectiveness of stemming for Turkish text retrieval (2000) 0.02

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Hull, D.; Ait-Mokhtar, S.; Chuat, M.; Eisele, A.; Gaussier, E.; Grefenstette, G.; Isabelle, P.; Samulesson, C.; Segand, F.: Language technologies and patent search and classification (2001) 0.02

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Vilar, P.; Dimec, J.: Krnjenje kot osnova nekaterih nekonvencionalnih metod poizvedovanja (2000) 0.02

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Perera, P.; Witte, R.: ¬A self-learning context-aware lemmatizer for German (2005) 0.01

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Ramisch, C.; Schreiner, P.; Idiart, M.; Villavicencio, A.: ¬An evaluation of methods for the extraction of multiword expressions (20xx) 0.01

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Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.01

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Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.01

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Date: 14. 8.2004 17:22:56
Source: Online. 28(2004) no.3, S.22-29

Hammwöhner, R.: TransRouter revisited : Decision support in the routing of translation projects (2000) 0.01

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Date: 10.12.2000 18:22:35

Paolillo, J.C.: Linguistics and the information sciences (2009) 0.01

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Date: 27. 8.2011 14:22:33

Schneider, R.: Web 3.0 ante portas? : Integration von Social Web und Semantic Web (2008) 0.01

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Date: 22. 1.2011 10:38:28

Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001) 0.01

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Cimiano, P.; Völker, J.; Studer, R.: Ontologies on demand? : a description of the state-of-the-art, applications, challenges and trends for ontology learning from text (2006) 0.01

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Argamon, S.; Whitelaw, C.; Chase, P.; Hota, S.R.; Garg, N.; Levitan, S.: Stylistic text classification using functional lexical features (2007) 0.01

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Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.01

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Date: 16. 2.2000 14:22:39

Kettunen, K.; Kunttu, T.; Järvelin, K.: To stem or lemmatize a highly inflectional language in a probabilistic IR environment? (2005) 0.01
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Abstract

Purpose - To show that stem generation compares well with lemmatization as a morphological tool for a highly inflectional language for IR purposes in a best-match retrieval system. Design/methodology/approach - Effects of three different morphological methods - lemmatization, stemming and stem production - for Finnish are compared in a probabilistic IR environment (INQUERY). Evaluation is done using a four-point relevance scale which is partitioned differently in different test settings. Findings - Results show that stem production, a lighter method than morphological lemmatization, compares well with lemmatization in a best-match IR environment. Differences in performance between stem production and lemmatization are small and they are not statistically significant in most of the tested settings. It is also shown that hitherto a rather neglected method of morphological processing for Finnish, stemming, performs reasonably well although the stemmer used - a Porter stemmer implementation - is far from optimal for a morphologically complex language like Finnish. In another series of tests, the effects of compound splitting and derivational expansion of queries are tested. Practical implications - Usefulness of morphological lemmatization and stem generation for IR purposes can be estimated with many factors. On the average P-R level they seem to behave very close to each other in a probabilistic IR system. Thus, the choice of the used method with highly inflectional languages needs to be estimated along other dimensions too. Originality/value - Results are achieved using Finnish as an example of a highly inflectional language. The results are of interest for anyone who is interested in processing of morphological variation of a highly inflected language for IR purposes.

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