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        <title>Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers</title>
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    <name type="personal">
        <namePart type="given">Stefania</namePart>
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    <name type="personal">
        <namePart type="given">Ekaterina</namePart>
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    <abstract>We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.</abstract>
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%0 Conference Proceedings
%T Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers
%A Degaetano-Ortlieb, Stefania
%A Fankhauser, Peter
%A Kermes, Hannah
%A Lapshinova-Koltunski, Ekaterina
%A Ordan, Noam
%A Teich, Elke
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F degaetano-ortlieb-etal-2014-data
%X We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/291_Paper.pdf
%P 1327-1334

Markdown (Informal)

[Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers](http://www.lrec-conf.org/proceedings/lrec2014/pdf/291_Paper.pdf) (Degaetano-Ortlieb et al., LREC 2014)

• Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers (Degaetano-Ortlieb et al., LREC 2014)

ACL

• Stefania Degaetano-Ortlieb, Peter Fankhauser, Hannah Kermes, Ekaterina Lapshinova-Koltunski, Noam Ordan, and Elke Teich. 2014. Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1327–1334, Reykjavik, Iceland. European Language Resources Association (ELRA).